├── .ipynb_checkpoints ├── MISA_Project_PreProcesing_Step(1)_Registration-checkpoint.ipynb ├── MISA_Project_PreProcesing_Step(2)_Normalization-checkpoint.ipynb └── PreparingTestingData-checkpoint.ipynb ├── Evaluation_MISA_Project.ipynb ├── Images ├── 5_2.png ├── Preprocessing_pipelines.PNG ├── architecture.PNG ├── example_preprpcessed.png ├── figures_forreport.png └── overlay_val14.png ├── Instructios_How_to_run.pdf ├── MISA_Project_PreProcesing_Step(1)_Registration.ipynb ├── MISA_Project_PreProcesing_Step(2)_Normalization.ipynb ├── MISA_Project_Report.pdf ├── PreparingTestingData.ipynb ├── README.md ├── model ├── README.md ├── config_all.json ├── config_fsl_fast.json ├── config_fsl_first.json ├── config_malp_em.json ├── config_malp_em_tissue.json ├── config_spm_tissue.json ├── config_tissue.json ├── deploy.pvpy ├── deploy.py ├── dim.nii ├── eval.ipynb ├── neuronet.py ├── neuronet.pyc ├── parse_csvs.ipynb ├── reader.py ├── reader.pyc ├── reader2.pyc ├── sandbox.ipynb ├── test.csv ├── train.csv ├── train.py └── val.csv └── paper_147.pptx /.ipynb_checkpoints/PreparingTestingData-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "toc": true 7 | }, 8 | "source": [ 9 | "

Table of Contents

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" 11 | ] 12 | }, 13 | { 14 | "cell_type": "markdown", 15 | "metadata": {}, 16 | "source": [ 17 | " Description & Instructions How to use this Script.\n", 18 | "\n", 19 | "* 1) Import the libraries running the Section \"Importing Libararies\"\n", 20 | "* 2) Function used in for histpgram matching and Normalization\n", 21 | "* 3) Apply the Histogram Maching\n", 22 | "* 4) Making 1mx1mx1m predicted Segmented Prediction to Original Spacing Using the prevviously saved Transformation Matrix" 23 | ] 24 | }, 25 | { 26 | "cell_type": "markdown", 27 | "metadata": { 28 | "heading_collapsed": true 29 | }, 30 | "source": [ 31 | "# Importing Libraries" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 1, 37 | "metadata": { 38 | "hidden": true 39 | }, 40 | "outputs": [ 41 | { 42 | "name": "stderr", 43 | "output_type": "stream", 44 | "text": [ 45 | "C:\\Users\\Fakrul-IslamTUSHAR\\Anaconda2\\envs\\nnet\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", 46 | " from ._conv import register_converters as _register_converters\n" 47 | ] 48 | } 49 | ], 50 | "source": [ 51 | "import SimpleITK as sitk\n", 52 | "import os\n", 53 | "import pandas as pd\n", 54 | "import numpy as np\n", 55 | "import glob\n", 56 | "import os\n", 57 | "import nibabel as nib\n", 58 | "\n", 59 | "from matplotlib import pyplot as plt\n", 60 | "from dltk.io.augmentation import *\n", 61 | "from dltk.io.preprocessing import *" 62 | ] 63 | }, 64 | { 65 | "cell_type": "markdown", 66 | "metadata": { 67 | "heading_collapsed": true 68 | }, 69 | "source": [ 70 | "# Function" 71 | ] 72 | }, 73 | { 74 | "cell_type": "markdown", 75 | "metadata": {}, 76 | "source": [ 77 | "# Histogram Matching" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": 2, 83 | "metadata": {}, 84 | "outputs": [], 85 | "source": [ 86 | "# -------------------------------------------------------\n", 87 | "##This functions were coppied from the github reprocesoty:https://github.com/sergivalverde/mri_utils\n", 88 | "# if you this code please refer this github.\n", 89 | "\n", 90 | "# Image processing functions\n", 91 | "# Useful for brain MRI analysis\n", 92 | "#\n", 93 | "# Sergi Valverde 2018\n", 94 | "# svalverde@eia.udg.edu\n", 95 | "#\n", 96 | "# -------------------------------------------------------\n", 97 | "\n", 98 | "import numpy as np\n", 99 | "from scipy.ndimage import label\n", 100 | "from scipy.ndimage import labeled_comprehension as lc\n", 101 | "import SimpleITK as sitk\n", 102 | "\n", 103 | "\n", 104 | "\n", 105 | "def histogram_matching(mov_scan, ref_scan,\n", 106 | " histogram_levels=2048,\n", 107 | " match_points=100,\n", 108 | " set_th_mean=True):\n", 109 | " \"\"\"\n", 110 | " Histogram matching following the method developed on\n", 111 | " Nyul et al 2001 (ITK implementation)\n", 112 | " inputs:\n", 113 | " - mov_scan: np.array containing the image to normalize\n", 114 | " - ref_scan np.array containing the reference image\n", 115 | " - histogram levels\n", 116 | " - number of matched points\n", 117 | " - Threshold Mean setting\n", 118 | " outputs:\n", 119 | " - histogram matched image\n", 120 | " \"\"\"\n", 121 | "\n", 122 | " # convert np arrays into itk image objects\n", 123 | " ref = sitk.GetImageFromArray(ref_scan.astype('float32'))\n", 124 | " mov = sitk.GetImageFromArray(mov_scan.astype('float32'))\n", 125 | "\n", 126 | " # perform histogram matching\n", 127 | " caster = sitk.CastImageFilter()\n", 128 | " caster.SetOutputPixelType(ref.GetPixelID())\n", 129 | "\n", 130 | " matcher = sitk.HistogramMatchingImageFilter()\n", 131 | " matcher.SetNumberOfHistogramLevels(histogram_levels)\n", 132 | " matcher.SetNumberOfMatchPoints(match_points)\n", 133 | " matcher.SetThresholdAtMeanIntensity(set_th_mean)\n", 134 | " matched_vol = matcher.Execute(mov, ref)\n", 135 | "\n", 136 | " return matched_vol" 137 | ] 138 | }, 139 | { 140 | "cell_type": "markdown", 141 | "metadata": {}, 142 | "source": [ 143 | "# Normalization and Histogram Matching" 144 | ] 145 | }, 146 | { 147 | "cell_type": "markdown", 148 | "metadata": {}, 149 | "source": [ 150 | "## Loading Reference data " 151 | ] 152 | }, 153 | { 154 | "cell_type": "code", 155 | "execution_count": 4, 156 | "metadata": {}, 157 | "outputs": [ 158 | { 159 | "name": "stderr", 160 | "output_type": "stream", 161 | "text": [ 162 | "C:\\Users\\Fakrul-IslamTUSHAR\\Anaconda2\\envs\\nnet\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", 163 | " \"\"\"\n" 164 | ] 165 | } 166 | ], 167 | "source": [ 168 | "mylist = pd.read_csv(\n", 169 | " \"MISAPreorocessingTestReg_info.csv\",\n", 170 | " dtype=object,\n", 171 | " keep_default_na=False,\n", 172 | " na_values=[]).as_matrix()" 173 | ] 174 | }, 175 | { 176 | "cell_type": "code", 177 | "execution_count": 5, 178 | "metadata": {}, 179 | "outputs": [], 180 | "source": [ 181 | "Save_Preprocessed_Test_data=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/save_processed_test_data/\"\n" 182 | ] 183 | }, 184 | { 185 | "cell_type": "code", 186 | "execution_count": 6, 187 | "metadata": {}, 188 | "outputs": [], 189 | "source": [ 190 | "ref_volume=sitk.ReadImage(\"ref_07.nii.gz\", sitk.sitkFloat32)\n", 191 | "ref_array = sitk.GetArrayFromImage(ref_volume)" 192 | ] 193 | }, 194 | { 195 | "cell_type": "code", 196 | "execution_count": 8, 197 | "metadata": {}, 198 | "outputs": [ 199 | { 200 | "name": "stdout", 201 | "output_type": "stream", 202 | "text": [ 203 | "IBSR_02\n", 204 | "########Saved#########\n", 205 | "IBSR_10\n", 206 | "########Saved#########\n", 207 | "IBSR_15\n", 208 | "########Saved#########\n" 209 | ] 210 | } 211 | ], 212 | "source": [ 213 | "for im in mylist:\n", 214 | " ###Getting Suvject MRI\n", 215 | " img_fn = str(im[1])\n", 216 | " img_name = img_fn.split('/')[-1].split('.')[0]\n", 217 | " print(img_name)\n", 218 | " #Image_Name.append(img_name)\n", 219 | " histoMached_imageName=img_name+'.nii.gz' \n", 220 | "\n", 221 | "# =============================================================================\n", 222 | "# load data\n", 223 | "# =============================================================================\n", 224 | " #Loading the image\n", 225 | " sitk_t1 = sitk.ReadImage(img_fn, sitk.sitkFloat32)\n", 226 | " t1 = sitk.GetArrayFromImage(sitk_t1)\n", 227 | " normalized_vol=normalise_zero_one(t1)\n", 228 | " \n", 229 | " \n", 230 | " Histo_mached_vol=histogram_matching(normalized_vol,ref_array)\n", 231 | " Histo_mached_vol.CopyInformation(sitk_t1)\n", 232 | " \n", 233 | " sitk.WriteImage(Histo_mached_vol, os.path.join(Save_Preprocessed_Test_data,histoMached_imageName))\n", 234 | " print(\"########Saved#########\")" 235 | ] 236 | }, 237 | { 238 | "cell_type": "code", 239 | "execution_count": 9, 240 | "metadata": {}, 241 | "outputs": [ 242 | { 243 | "name": "stdout", 244 | "output_type": "stream", 245 | "text": [ 246 | "[['IBSR_02'\n", 247 | " 'C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata/IBSR_02.nii.gz'\n", 248 | " 'C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_02.tfm']\n", 249 | " ['IBSR_10'\n", 250 | " 'C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata/IBSR_10.nii.gz'\n", 251 | " 'C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_10.tfm']\n", 252 | " ['IBSR_15'\n", 253 | " 'C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata/IBSR_15.nii.gz'\n", 254 | " 'C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_15.tfm']]\n" 255 | ] 256 | } 257 | ], 258 | "source": [ 259 | "print(mylist)" 260 | ] 261 | }, 262 | { 263 | "cell_type": "markdown", 264 | "metadata": { 265 | "heading_collapsed": true 266 | }, 267 | "source": [ 268 | "# Making the labels back to original Spacing" 269 | ] 270 | }, 271 | { 272 | "cell_type": "markdown", 273 | "metadata": { 274 | "hidden": true 275 | }, 276 | "source": [ 277 | "For This Process Following steps need to be done.\n", 278 | "\n", 279 | "* 1) Put path of the Prediction(Segmented Nifti file) in Section 5.1 \"Final_Seg_test_path\"\n", 280 | "* 2) Put path of the RAW DATA(Nifti file) in Section 5.2 \"Test_data_raw_path\"\n", 281 | "* 3) Put path of the Saved Transformation Matrics(Nifti file) in Section 5.3 \"tmf_path\"\n", 282 | "* 4) put the desired folder path were you want to save the Segmented nifti with the original spacing \"path_to_save_images\" and RUN." 283 | ] 284 | }, 285 | { 286 | "cell_type": "markdown", 287 | "metadata": { 288 | "heading_collapsed": true, 289 | "hidden": true 290 | }, 291 | "source": [ 292 | "## Segmented Results" 293 | ] 294 | }, 295 | { 296 | "cell_type": "code", 297 | "execution_count": 35, 298 | "metadata": { 299 | "hidden": true 300 | }, 301 | "outputs": [ 302 | { 303 | "name": "stdout", 304 | "output_type": "stream", 305 | "text": [ 306 | "['IBSR_02_seg.nii.gz', 'IBSR_10_seg.nii.gz', 'IBSR_15_seg.nii.gz']\n" 307 | ] 308 | } 309 | ], 310 | "source": [ 311 | "Final_Seg_test_path=\"H:/f_r/final_test/\"\n", 312 | "My_Predicted_Seg_list=os.listdir(Final_Seg_test_path)\n", 313 | "print(My_Predicted_Seg_list)\n", 314 | "\n", 315 | "#complete_Segmented_data=Final_Seg_test_path+My_Predicted_Seg_list[0]\n", 316 | "#print(Segmented_data)" 317 | ] 318 | }, 319 | { 320 | "cell_type": "markdown", 321 | "metadata": { 322 | "heading_collapsed": true, 323 | "hidden": true 324 | }, 325 | "source": [ 326 | "## Raw Test Data " 327 | ] 328 | }, 329 | { 330 | "cell_type": "code", 331 | "execution_count": 36, 332 | "metadata": { 333 | "hidden": true 334 | }, 335 | "outputs": [ 336 | { 337 | "name": "stdout", 338 | "output_type": "stream", 339 | "text": [ 340 | "['IBSR_02.nii.gz', 'IBSR_10.nii.gz', 'IBSR_15.nii.gz']\n" 341 | ] 342 | } 343 | ], 344 | "source": [ 345 | "Test_data_raw_path=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Test_Data_Raw/\"\n", 346 | "Test_data_list=os.listdir(Test_data_raw_path)\n", 347 | "print(Test_data_list)\n", 348 | "\n", 349 | "#complete_RAW_data=Test_data_raw_path+Test_data_list[0]\n", 350 | "#print(complete_RAW_data)" 351 | ] 352 | }, 353 | { 354 | "cell_type": "markdown", 355 | "metadata": { 356 | "heading_collapsed": true, 357 | "hidden": true 358 | }, 359 | "source": [ 360 | "## Transformation Matrix" 361 | ] 362 | }, 363 | { 364 | "cell_type": "code", 365 | "execution_count": 37, 366 | "metadata": { 367 | "hidden": true 368 | }, 369 | "outputs": [ 370 | { 371 | "name": "stdout", 372 | "output_type": "stream", 373 | "text": [ 374 | "['IBSR_02.tfm', 'IBSR_10.tfm', 'IBSR_15.tfm']\n" 375 | ] 376 | } 377 | ], 378 | "source": [ 379 | "tmf_path=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/\"\n", 380 | "tmf_data_list=os.listdir(tmf_path)\n", 381 | "print(tmf_data_list)" 382 | ] 383 | }, 384 | { 385 | "cell_type": "markdown", 386 | "metadata": { 387 | "heading_collapsed": true, 388 | "hidden": true 389 | }, 390 | "source": [ 391 | "## Inverse Registration" 392 | ] 393 | }, 394 | { 395 | "cell_type": "code", 396 | "execution_count": 41, 397 | "metadata": { 398 | "hidden": true 399 | }, 400 | "outputs": [ 401 | { 402 | "name": "stdout", 403 | "output_type": "stream", 404 | "text": [ 405 | "H:/f_r/final_test/IBSR_02_seg.nii.gz\n", 406 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Test_Data_Raw/IBSR_02.nii.gz\n", 407 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_02.tfm\n", 408 | "IBSR_02\n", 409 | "H:/f_r/final_test/IBSR_10_seg.nii.gz\n", 410 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Test_Data_Raw/IBSR_10.nii.gz\n", 411 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_10.tfm\n", 412 | "IBSR_10\n", 413 | "H:/f_r/final_test/IBSR_15_seg.nii.gz\n", 414 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Test_Data_Raw/IBSR_15.nii.gz\n", 415 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_15.tfm\n", 416 | "IBSR_15\n" 417 | ] 418 | } 419 | ], 420 | "source": [ 421 | "path_to_save_images=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/submission/\"\n", 422 | "\n", 423 | "for im in range(0,3):\n", 424 | " \n", 425 | " ##Creating Data Path\n", 426 | " complete_Segmented_data=Final_Seg_test_path+My_Predicted_Seg_list[im]\n", 427 | " complete_RAW_data=Test_data_raw_path+Test_data_list[im]\n", 428 | " complete_tmf_data=tmf_path+tmf_data_list[im]\n", 429 | " \n", 430 | " print(complete_Segmented_data)\n", 431 | " print(complete_RAW_data)\n", 432 | " print(complete_tmf_data)\n", 433 | " \n", 434 | " ###Getting Suvject MRI\n", 435 | " img_fn = str(complete_RAW_data)\n", 436 | " img_name = img_fn.split('/')[-1].split('.')[0]\n", 437 | " label_fn=str(complete_Segmented_data)\n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " print(img_name)\n", 442 | " \n", 443 | " ##Creating Name\n", 444 | " Registered_imageName=img_name+\"_seg\"+'.nii.gz'\n", 445 | " \n", 446 | " ##The Original Spaced Image\n", 447 | " Original_fixed=sitk.ReadImage(img_fn, sitk.sitkFloat32)\n", 448 | " \n", 449 | " ####Segmented Prediction\n", 450 | " label_Registered_moving=sitk.ReadImage(label_fn, sitk.sitkFloat32)\n", 451 | " \n", 452 | " ######Load the Transformation\n", 453 | " initial_transform_for_InterTransformation=sitk.ReadTransform(complete_tmf_data)\n", 454 | " inverse_Transformation=initial_transform_for_InterTransformation.GetInverse()\n", 455 | " \n", 456 | " Original_resampled_label = sitk.Resample(label_Registered_moving, Original_fixed, \n", 457 | " inverse_Transformation, sitk.sitkNearestNeighbor, 0.0, \n", 458 | " label_Registered_moving.GetPixelID())\n", 459 | " \n", 460 | " sitk.WriteImage(Original_resampled_label, os.path.join(path_to_save_images, Registered_imageName))\n", 461 | " \n", 462 | " \n", 463 | " " 464 | ] 465 | }, 466 | { 467 | "cell_type": "code", 468 | "execution_count": null, 469 | "metadata": { 470 | "hidden": true 471 | }, 472 | "outputs": [], 473 | "source": [] 474 | } 475 | ], 476 | "metadata": { 477 | "kernelspec": { 478 | "display_name": "Python 3", 479 | "language": "python", 480 | "name": "python3" 481 | }, 482 | "language_info": { 483 | "codemirror_mode": { 484 | "name": "ipython", 485 | "version": 3 486 | }, 487 | "file_extension": ".py", 488 | "mimetype": "text/x-python", 489 | "name": "python", 490 | "nbconvert_exporter": "python", 491 | "pygments_lexer": "ipython3", 492 | "version": "3.6.7" 493 | }, 494 | "toc": { 495 | "base_numbering": 1, 496 | "nav_menu": {}, 497 | "number_sections": true, 498 | "sideBar": true, 499 | "skip_h1_title": false, 500 | "title_cell": "Table of Contents", 501 | "title_sidebar": "Contents", 502 | "toc_cell": true, 503 | "toc_position": {}, 504 | "toc_section_display": true, 505 | "toc_window_display": true 506 | }, 507 | "varInspector": { 508 | "cols": { 509 | "lenName": 16, 510 | "lenType": 16, 511 | "lenVar": 40 512 | }, 513 | "kernels_config": { 514 | "python": { 515 | "delete_cmd_postfix": "", 516 | "delete_cmd_prefix": "del ", 517 | "library": "var_list.py", 518 | "varRefreshCmd": "print(var_dic_list())" 519 | }, 520 | "r": { 521 | "delete_cmd_postfix": ") ", 522 | "delete_cmd_prefix": "rm(", 523 | "library": "var_list.r", 524 | "varRefreshCmd": "cat(var_dic_list()) " 525 | } 526 | }, 527 | "types_to_exclude": [ 528 | "module", 529 | "function", 530 | "builtin_function_or_method", 531 | "instance", 532 | "_Feature" 533 | ], 534 | "window_display": false 535 | } 536 | }, 537 | "nbformat": 4, 538 | "nbformat_minor": 2 539 | } 540 | -------------------------------------------------------------------------------- /Images/5_2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fitushar/Brain-Tissue-Segmentation-Using-Deep-Learning-Pipeline-NeuroNet/e3d33136d34a9ea4b55b3e210c78271980b683e2/Images/5_2.png -------------------------------------------------------------------------------- /Images/Preprocessing_pipelines.PNG: -------------------------------------------------------------------------------- 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Table of Contents

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" 11 | ] 12 | }, 13 | { 14 | "cell_type": "markdown", 15 | "metadata": {}, 16 | "source": [ 17 | "This Notebook is to 1st Preprocessing Steps for the MISA project. Registering the Training and Validation Data to\n", 18 | " MNI 1mm Template (MNI152_T1_1mm_Brain.nii.gz) " 19 | ] 20 | }, 21 | { 22 | "cell_type": "markdown", 23 | "metadata": { 24 | "heading_collapsed": true 25 | }, 26 | "source": [ 27 | "# Import Libraries" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 1, 33 | "metadata": { 34 | "hidden": true 35 | }, 36 | "outputs": [ 37 | { 38 | "name": "stderr", 39 | "output_type": "stream", 40 | "text": [ 41 | "C:\\Users\\Fakrul-IslamTUSHAR\\Anaconda2\\envs\\nnet\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", 42 | " from ._conv import register_converters as _register_converters\n" 43 | ] 44 | } 45 | ], 46 | "source": [ 47 | "import os\n", 48 | "import numpy as np\n", 49 | "import nibabel as nib\n", 50 | "import matplotlib.pyplot as plt\n", 51 | "from sklearn.cluster import KMeans\n", 52 | "from numpy.linalg import inv, det, norm\n", 53 | "from math import sqrt, pi\n", 54 | "from functools import partial\n", 55 | "from scipy.spatial.distance import dice\n", 56 | "import time\n", 57 | "import operator\n", 58 | "import matplotlib.pyplot as plt\n", 59 | "import SimpleITK as sitk\n", 60 | "import pandas as pd\n", 61 | "import seaborn as sns\n", 62 | "%matplotlib inline" 63 | ] 64 | }, 65 | { 66 | "cell_type": "markdown", 67 | "metadata": { 68 | "heading_collapsed": true 69 | }, 70 | "source": [ 71 | "# Reading the Training and validation Data" 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": 2, 77 | "metadata": { 78 | "hidden": true 79 | }, 80 | "outputs": [ 81 | { 82 | "name": "stderr", 83 | "output_type": "stream", 84 | "text": [ 85 | "C:\\Users\\Fakrul-IslamTUSHAR\\Anaconda2\\envs\\nnet\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", 86 | " \"\"\"\n" 87 | ] 88 | } 89 | ], 90 | "source": [ 91 | "mylist = pd.read_csv(\n", 92 | " \"TrainAndValidation.csv\",\n", 93 | " dtype=object,\n", 94 | " keep_default_na=False,\n", 95 | " na_values=[]).as_matrix()" 96 | ] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": 3, 101 | "metadata": { 102 | "hidden": true 103 | }, 104 | "outputs": [ 105 | { 106 | "name": "stdout", 107 | "output_type": "stream", 108 | "text": [ 109 | "['1'\n", 110 | " 'C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/TrainingValidationTestSets/Training_Set/IBSR_01/IBSR_01.nii'\n", 111 | " 'C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/TrainingValidationTestSets/Training_Set/IBSR_01/IBSR_01_seg.nii']\n" 112 | ] 113 | } 114 | ], 115 | "source": [ 116 | "print(mylist[0])" 117 | ] 118 | }, 119 | { 120 | "cell_type": "markdown", 121 | "metadata": { 122 | "heading_collapsed": true 123 | }, 124 | "source": [ 125 | "# Function For registration" 126 | ] 127 | }, 128 | { 129 | "cell_type": "markdown", 130 | "metadata": { 131 | "hidden": true 132 | }, 133 | "source": [ 134 | "Helper Function for the registartion" 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": 4, 140 | "metadata": { 141 | "hidden": true 142 | }, 143 | "outputs": [], 144 | "source": [ 145 | "from ipywidgets import interact, fixed\n", 146 | "from IPython.display import clear_output\n", 147 | "\n", 148 | "# =============================================================================\n", 149 | "# Function Definitions\n", 150 | "# =============================================================================\n", 151 | "\n", 152 | "# Callback invoked by the interact IPython method for scrolling through the image stacks of\n", 153 | "# the two images (moving and fixed).\n", 154 | "def display_images(fixed_image_z, moving_image_z, fixed_npa, moving_npa):\n", 155 | " # Create a figure with two subplots and the specified size.\n", 156 | " plt.subplots(1,2,figsize=(10,8))\n", 157 | " \n", 158 | " # Draw the fixed image in the first subplot.\n", 159 | " plt.subplot(1,2,1)\n", 160 | " plt.imshow(fixed_npa[fixed_image_z,:,:],cmap=plt.cm.Greys_r);\n", 161 | " plt.title('fixed image')\n", 162 | " plt.axis('off')\n", 163 | " \n", 164 | " # Draw the moving image in the second subplot.\n", 165 | " plt.subplot(1,2,2)\n", 166 | " plt.imshow(moving_npa[moving_image_z,:,:],cmap=plt.cm.Greys_r);\n", 167 | " plt.title('moving image')\n", 168 | " plt.axis('off')\n", 169 | " \n", 170 | " plt.show()\n", 171 | "\n", 172 | "# Callback invoked by the IPython interact method for scrolling and modifying the alpha blending\n", 173 | "# of an image stack of two images that occupy the same physical space. \n", 174 | "def display_images_with_alpha(image_z, alpha, fixed, moving):\n", 175 | " img = (1.0 - alpha)*fixed[:,:,image_z] + alpha*moving[:,:,image_z] \n", 176 | " plt.imshow(sitk.GetArrayViewFromImage(img),cmap=plt.cm.Greys_r);\n", 177 | " plt.axis('off')\n", 178 | " plt.show()\n", 179 | " \n", 180 | "# Callback invoked when the StartEvent happens, sets up our new data.\n", 181 | "def start_plot():\n", 182 | " global metric_values, multires_iterations\n", 183 | " \n", 184 | " metric_values = []\n", 185 | " multires_iterations = []\n", 186 | "\n", 187 | "# Callback invoked when the EndEvent happens, do cleanup of data and figure.\n", 188 | "def end_plot():\n", 189 | " global metric_values, multires_iterations\n", 190 | " \n", 191 | " del metric_values\n", 192 | " del multires_iterations\n", 193 | " # Close figure, we don't want to get a duplicate of the plot latter on.\n", 194 | " plt.close()\n", 195 | "\n", 196 | "# Callback invoked when the IterationEvent happens, update our data and display new figure. \n", 197 | "def plot_values(registration_method):\n", 198 | " global metric_values, multires_iterations\n", 199 | " \n", 200 | " metric_values.append(registration_method.GetMetricValue()) \n", 201 | " # Clear the output area (wait=True, to reduce flickering), and plot current data\n", 202 | " clear_output(wait=True)\n", 203 | " # Plot the similarity metric values\n", 204 | " plt.plot(metric_values, 'r')\n", 205 | " plt.plot(multires_iterations, [metric_values[index] for index in multires_iterations], 'b*')\n", 206 | " plt.xlabel('Iteration Number',fontsize=12)\n", 207 | " plt.ylabel('Metric Value',fontsize=12)\n", 208 | " plt.show()\n", 209 | " \n", 210 | "# Callback invoked when the sitkMultiResolutionIterationEvent happens, update the index into the \n", 211 | "# metric_values list. \n", 212 | "def update_multires_iterations():\n", 213 | " global metric_values, multires_iterations\n", 214 | " multires_iterations.append(len(metric_values))\n", 215 | " " 216 | ] 217 | }, 218 | { 219 | "cell_type": "markdown", 220 | "metadata": { 221 | "heading_collapsed": true 222 | }, 223 | "source": [ 224 | "# Performing Registration" 225 | ] 226 | }, 227 | { 228 | "cell_type": "code", 229 | "execution_count": 10, 230 | "metadata": { 231 | "hidden": true 232 | }, 233 | "outputs": [], 234 | "source": [ 235 | "#Defininf The Output Folder\n", 236 | "Output_Registered_image_path='C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/'\n", 237 | "Output_Registered_label_path='C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/'\n", 238 | "Output_Registered_transformation_path='C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/'" 239 | ] 240 | }, 241 | { 242 | "cell_type": "code", 243 | "execution_count": 12, 244 | "metadata": { 245 | "hidden": true 246 | }, 247 | "outputs": [ 248 | { 249 | "data": { 250 | "image/png": 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\n", 251 | "text/plain": [ 252 | "
" 253 | ] 254 | }, 255 | "metadata": {}, 256 | "output_type": "display_data" 257 | }, 258 | { 259 | "name": "stdout", 260 | "output_type": "stream", 261 | "text": [ 262 | "Final metric value: -0.5335141688363023\n", 263 | "Optimizer's stopping condition, GradientDescentOptimizerv4Template: Convergence checker passed at iteration 9.\n" 264 | ] 265 | }, 266 | { 267 | "data": { 268 | "application/vnd.jupyter.widget-view+json": { 269 | "model_id": "2d49e3267df847f99c5aa0f920d56fb1", 270 | "version_major": 2, 271 | "version_minor": 0 272 | }, 273 | "text/plain": [ 274 | "interactive(children=(IntSlider(value=91, description='image_z', max=182), FloatSlider(value=0.5, description=…" 275 | ] 276 | }, 277 | "metadata": {}, 278 | "output_type": "display_data" 279 | }, 280 | { 281 | "data": { 282 | "application/vnd.jupyter.widget-view+json": { 283 | "model_id": "1fba8c45bb544702ab8e09d4259ee492", 284 | "version_major": 2, 285 | "version_minor": 0 286 | }, 287 | "text/plain": [ 288 | "interactive(children=(IntSlider(value=91, description='image_z', max=182), FloatSlider(value=0.5, description=…" 289 | ] 290 | }, 291 | "metadata": {}, 292 | "output_type": "display_data" 293 | } 294 | ], 295 | "source": [ 296 | "fixed_image = sitk.ReadImage('MNI152_T1_1mm_Brain.nii.gz', sitk.sitkFloat32)\n", 297 | "for im in mylist:\n", 298 | " ###Getting Suvject MRI\n", 299 | " img_fn = str(im[1])\n", 300 | " label_fn=str(im[2])\n", 301 | " img_name = img_fn.split('/')[-1].split('.')[0]\n", 302 | " label_name = label_fn.split('/')[-1].split('.')[0]\n", 303 | " print(img_name)\n", 304 | " print(label_name)\n", 305 | " \n", 306 | " ########### Output Name & Folder ##########\n", 307 | " Registered_imageName=img_name+'.nii.gz'\n", 308 | " Registered_labelName=label_name+'.nii.gz'\n", 309 | " Transformation_imageName=img_name+'.tfm'\n", 310 | " \n", 311 | " # =============================================================================\n", 312 | " # Loading the data\n", 313 | " # =============================================================================\n", 314 | " moving_image = sitk.ReadImage(img_fn, sitk.sitkFloat32)\n", 315 | " interact(display_images, fixed_image_z=(0,fixed_image.GetSize()[2]-1), moving_image_z=(0,moving_image.GetSize()[2]-1), fixed_npa = fixed(sitk.GetArrayViewFromImage(fixed_image)), moving_npa=fixed(sitk.GetArrayViewFromImage(moving_image)));\n", 316 | " # =============================================================================\n", 317 | " # initial Alighment \n", 318 | " # =============================================================================\n", 319 | " initial_transform = sitk.CenteredTransformInitializer(fixed_image, \n", 320 | " moving_image, \n", 321 | " sitk.Euler3DTransform(), \n", 322 | " sitk.CenteredTransformInitializerFilter.GEOMETRY)\n", 323 | "\n", 324 | " moving_resampled = sitk.Resample(moving_image, fixed_image, initial_transform, sitk.sitkNearestNeighbor, 0.0, moving_image.GetPixelID())\n", 325 | "\n", 326 | " interact(display_images_with_alpha, image_z=(0,fixed_image.GetSize()[2]), alpha=(0.0,1.0,0.05), fixed = fixed(fixed_image), moving=fixed(moving_resampled));\n", 327 | " # =============================================================================\n", 328 | " # Registration\n", 329 | " # =============================================================================\n", 330 | " registration_method = sitk.ImageRegistrationMethod()\n", 331 | "\n", 332 | " # Similarity metric settings.\n", 333 | " registration_method.SetMetricAsMattesMutualInformation(numberOfHistogramBins=50)\n", 334 | " registration_method.SetMetricSamplingStrategy(registration_method.RANDOM)\n", 335 | " registration_method.SetMetricSamplingPercentage(0.01)\n", 336 | "\n", 337 | " registration_method.SetInterpolator(sitk.sitkNearestNeighbor)\n", 338 | "\n", 339 | " # Optimizer settings.\n", 340 | " registration_method.SetOptimizerAsGradientDescent(learningRate=1.0, numberOfIterations=100, convergenceMinimumValue=1e-6, convergenceWindowSize=10)\n", 341 | " registration_method.SetOptimizerScalesFromPhysicalShift()\n", 342 | "\n", 343 | " # Setup for the multi-resolution framework. \n", 344 | " registration_method.SetShrinkFactorsPerLevel(shrinkFactors = [4,2,1])\n", 345 | " registration_method.SetSmoothingSigmasPerLevel(smoothingSigmas=[2,1,0])\n", 346 | " registration_method.SmoothingSigmasAreSpecifiedInPhysicalUnitsOn()\n", 347 | "\n", 348 | " # Don't optimize in-place, we would possibly like to run this cell multiple times.\n", 349 | " registration_method.SetInitialTransform(initial_transform, inPlace=False)\n", 350 | "\n", 351 | " # Connect all of the observers so that we can perform plotting during registration.\n", 352 | " registration_method.AddCommand(sitk.sitkStartEvent, start_plot)\n", 353 | " registration_method.AddCommand(sitk.sitkEndEvent, end_plot)\n", 354 | " registration_method.AddCommand(sitk.sitkMultiResolutionIterationEvent, update_multires_iterations) \n", 355 | " registration_method.AddCommand(sitk.sitkIterationEvent, lambda: plot_values(registration_method))\n", 356 | "\n", 357 | " final_transform = registration_method.Execute(sitk.Cast(fixed_image, sitk.sitkFloat32), \n", 358 | " sitk.Cast(moving_image, sitk.sitkFloat32))\n", 359 | "\n", 360 | " # =============================================================================\n", 361 | " # post processing Analysis\n", 362 | " # =============================================================================\n", 363 | " print('Final metric value: {0}'.format(registration_method.GetMetricValue()))\n", 364 | " print('Optimizer\\'s stopping condition, {0}'.format(registration_method.GetOptimizerStopConditionDescription()))\n", 365 | " #Visualize Expected Results\n", 366 | "\n", 367 | " moving_resampled = sitk.Resample(moving_image, fixed_image, final_transform, sitk.sitkNearestNeighbor, 0.0, moving_image.GetPixelID())\n", 368 | "\n", 369 | " interact(display_images_with_alpha, image_z=(0,fixed_image.GetSize()[2]), alpha=(0.0,1.0,0.05), fixed = fixed(fixed_image), moving=fixed(moving_resampled));\n", 370 | "\n", 371 | " sitk.WriteImage(moving_resampled, os.path.join(Output_Registered_image_path, Registered_imageName))\n", 372 | " sitk.WriteTransform(final_transform,os.path.join(Output_Registered_transformation_path,Transformation_imageName))\n", 373 | " \n", 374 | " # =============================================================================\n", 375 | " # Label Registration With the Previous Transformation\n", 376 | " # =============================================================================\n", 377 | " ###Read The label\n", 378 | " moving_label=sitk.ReadImage(label_fn, sitk.sitkFloat32)\n", 379 | " ###Get the Transformation\n", 380 | " transform=final_transform\n", 381 | " \n", 382 | " ###resampling the labels\n", 383 | " moving_resampled_label = sitk.Resample(moving_label, fixed_image, transform, sitk.sitkNearestNeighbor, 0.0, moving_label.GetPixelID())\n", 384 | "\n", 385 | " interact(display_images_with_alpha, image_z=(0,fixed_image.GetSize()[2]), alpha=(0.0,1.0,0.05), fixed = fixed(fixed_image), moving=fixed(moving_resampled_label));\n", 386 | "\n", 387 | " sitk.WriteImage(moving_resampled_label, os.path.join(Output_Registered_label_path,Registered_labelName))" 388 | ] 389 | }, 390 | { 391 | "cell_type": "markdown", 392 | "metadata": { 393 | "heading_collapsed": true 394 | }, 395 | "source": [ 396 | "# Dice Similarity Fuction " 397 | ] 398 | }, 399 | { 400 | "cell_type": "code", 401 | "execution_count": 16, 402 | "metadata": { 403 | "hidden": true 404 | }, 405 | "outputs": [], 406 | "source": [ 407 | "def dice_similarity(Seg_img, GT_img,state):\n", 408 | " \"\"\" \n", 409 | " Inputs:\n", 410 | " Seg_img (numpy.ndarray): Segmented Image.\n", 411 | " GT_img (numpy.ndarray): Ground Truth Image.\n", 412 | " State: \"nifti\" if the images are nifti file\n", 413 | " \"arr\" if the images are an ndarray\n", 414 | " output:\n", 415 | " Dice Similarity Coefficient: dice_CSF, dice_GM, dice_WM.\"\"\"\n", 416 | " \n", 417 | " if (state==\"nifti\"):\n", 418 | " segmented_data = Seg_img.get_data().copy()\n", 419 | " groundtruth_data = GT_img.get_data().copy()\n", 420 | " elif (state==\"arr\"):\n", 421 | " segmented_data = Seg_img.copy()\n", 422 | " groundtruth_data = GT_img.copy()\n", 423 | " \n", 424 | " #Calculte DICE\n", 425 | " def dice_coefficient(SI,GT):\n", 426 | " # 2 * TP / (FN + (2 * TP) + FP)\n", 427 | " intersection = np.logical_and(SI, GT)\n", 428 | " return 2. * intersection.sum() / (SI.sum() + GT.sum())\n", 429 | " \n", 430 | " #Dice for CSF\n", 431 | " Seg_CSF = (segmented_data == 1) * 1\n", 432 | " GT_CSF = (groundtruth_data == 1) * 1\n", 433 | " dice_CSF = dice_coefficient(Seg_CSF, GT_CSF)\n", 434 | " #Dice for GM\n", 435 | " Seg_GM = (segmented_data == 2) * 1\n", 436 | " GT_GM = (groundtruth_data == 2) * 1\n", 437 | " dice_GM = dice_coefficient(Seg_GM, GT_GM)\n", 438 | " #Dice for WM\n", 439 | " Seg_WM = (segmented_data == 3) * 1\n", 440 | " GT_WM = (groundtruth_data == 3) * 1\n", 441 | " dice_WM = dice_coefficient(Seg_WM, GT_WM)\n", 442 | " \n", 443 | " return dice_CSF, dice_GM, dice_WM" 444 | ] 445 | }, 446 | { 447 | "cell_type": "markdown", 448 | "metadata": { 449 | "heading_collapsed": true 450 | }, 451 | "source": [ 452 | "# Cheaking inverse registration will work or not" 453 | ] 454 | }, 455 | { 456 | "cell_type": "code", 457 | "execution_count": 13, 458 | "metadata": { 459 | "hidden": true 460 | }, 461 | "outputs": [ 462 | { 463 | "name": "stdout", 464 | "output_type": "stream", 465 | "text": [ 466 | "['IBSR_01_seg.nii.gz', 'IBSR_03_seg.nii.gz', 'IBSR_04_seg.nii.gz', 'IBSR_05_seg.nii.gz', 'IBSR_06_seg.nii.gz', 'IBSR_07_seg.nii.gz', 'IBSR_08_seg.nii.gz', 'IBSR_09_seg.nii.gz', 'IBSR_11_seg.nii.gz', 'IBSR_12_seg.nii.gz', 'IBSR_13_seg.nii.gz', 'IBSR_14_seg.nii.gz', 'IBSR_16_seg.nii.gz', 'IBSR_17_seg.nii.gz', 'IBSR_18_seg.nii.gz']\n" 467 | ] 468 | } 469 | ], 470 | "source": [ 471 | "My_Registered_label_list=os.listdir(Output_Registered_label_path)\n", 472 | "print(My_Registered_label_list)" 473 | ] 474 | }, 475 | { 476 | "cell_type": "code", 477 | "execution_count": 14, 478 | "metadata": { 479 | "hidden": true 480 | }, 481 | "outputs": [ 482 | { 483 | "name": "stdout", 484 | "output_type": "stream", 485 | "text": [ 486 | "['IBSR_01.tfm', 'IBSR_03.tfm', 'IBSR_04.tfm', 'IBSR_05.tfm', 'IBSR_06.tfm', 'IBSR_07.tfm', 'IBSR_08.tfm', 'IBSR_09.tfm', 'IBSR_11.tfm', 'IBSR_12.tfm', 'IBSR_13.tfm', 'IBSR_14.tfm', 'IBSR_16.tfm', 'IBSR_17.tfm', 'IBSR_18.tfm']\n" 487 | ] 488 | } 489 | ], 490 | "source": [ 491 | "My_Registered_Trans_File=os.listdir(Output_Registered_transformation_path)\n", 492 | "print(My_Registered_Trans_File)" 493 | ] 494 | }, 495 | { 496 | "cell_type": "code", 497 | "execution_count": 24, 498 | "metadata": { 499 | "hidden": true, 500 | "scrolled": true 501 | }, 502 | "outputs": [ 503 | { 504 | "name": "stdout", 505 | "output_type": "stream", 506 | "text": [ 507 | "#############Loading Results If we Do inverse Registration############### \n", 508 | "data: IBSR_01\n", 509 | "CSF DICE = 0.9825621309899767 GM DICE = 0.9912496715782244 WM DICE = 0.9880310024988352\n", 510 | "############################ \n", 511 | "data: IBSR_03\n", 512 | "CSF DICE = 0.9697968972494941 GM DICE = 0.991009179006726 WM DICE = 0.9870380013130924\n", 513 | "############################ \n", 514 | "data: IBSR_04\n", 515 | "CSF DICE = 0.9775035832242787 GM DICE = 0.991514231804458 WM DICE = 0.987651804670913\n", 516 | "############################ \n", 517 | "data: IBSR_05\n", 518 | "CSF DICE = 0.9825619414371867 GM DICE = 0.9900341642668938 WM DICE = 0.9893874286823152\n", 519 | "############################ \n", 520 | "data: IBSR_06\n", 521 | "CSF DICE = 0.9862122788761707 GM DICE = 0.9866776751765345 WM DICE = 0.9871989311140422\n", 522 | "############################ \n", 523 | "data: IBSR_07\n", 524 | "CSF DICE = 0.998586537992884 GM DICE = 0.9994064054436964 WM DICE = 0.9993687508790062\n", 525 | "############################ \n", 526 | "data: IBSR_08\n", 527 | "CSF DICE = 0.9986510373057959 GM DICE = 0.9985595970632075 WM DICE = 0.9984952139443508\n", 528 | "############################ \n", 529 | "data: IBSR_09\n", 530 | "CSF DICE = 0.9968720448097766 GM DICE = 0.9984399123036154 WM DICE = 0.9983404133068171\n", 531 | "############################ \n", 532 | "data: IBSR_16\n", 533 | "CSF DICE = 0.9507329161241548 GM DICE = 0.9824083459155735 WM DICE = 0.9735339391230945\n", 534 | "############################ \n", 535 | "data: IBSR_18\n", 536 | "CSF DICE = 0.9591194295136732 GM DICE = 0.9817603190815379 WM DICE = 0.9746477068175066\n", 537 | "############################ \n", 538 | "data: IBSR_11\n", 539 | "CSF DICE = 0.997624703087886 GM DICE = 0.9985968288140679 WM DICE = 0.9986385574634562\n", 540 | "############################ \n", 541 | "data: IBSR_12\n", 542 | "CSF DICE = 0.9964391691394658 GM DICE = 0.9980941459737718 WM DICE = 0.9977821939864278\n", 543 | "############################ \n", 544 | "data: IBSR_13\n", 545 | "CSF DICE = 0.9747929583201339 GM DICE = 0.9918151106915374 WM DICE = 0.9866391946408345\n", 546 | "############################ \n", 547 | "data: IBSR_14\n", 548 | "CSF DICE = 0.978958385967445 GM DICE = 0.9904732572623514 WM DICE = 0.9878724958249686\n", 549 | "############################ \n", 550 | "data: IBSR_17\n", 551 | "CSF DICE = 0.9666451820818562 GM DICE = 0.9812091136119974 WM DICE = 0.9711705490642771\n", 552 | "############################ \n" 553 | ] 554 | } 555 | ], 556 | "source": [ 557 | "print(\"#############Loading Results If we Do inverse Registration############### \")\n", 558 | "\n", 559 | "for im in mylist:\n", 560 | " ###Getting Suvject MRI\n", 561 | " img_fn = str(im[1])\n", 562 | " label_fn=str(im[2])\n", 563 | " img_name = img_fn.split('/')[-1].split('.')[0]\n", 564 | " label_name = label_fn.split('/')[-1].split('.')[0]\n", 565 | " \n", 566 | " ##The Original Spaced Image\n", 567 | " label_Original_fixed=sitk.ReadImage(label_fn, sitk.sitkFloat32)\n", 568 | " ###REgisted MNI label\n", 569 | " label_Registered_moving=sitk.ReadImage(Output_Registered_label_path+label_name+'.nii.gz', sitk.sitkFloat32)\n", 570 | " #print(Output_Registered_label_path+label_name+'.nii.gz')\n", 571 | " \n", 572 | " ######Load the Transformation\n", 573 | " initial_transform_for_InterTransformation=sitk.ReadTransform(Output_Registered_transformation_path+img_name+'.tfm')\n", 574 | " inverse_Transformation=initial_transform_for_InterTransformation.GetInverse()\n", 575 | " \n", 576 | " Original_resampled_label = sitk.Resample(label_Registered_moving, label_Original_fixed, inverse_Transformation, sitk.sitkNearestNeighbor, 0.0, label_Registered_moving.GetPixelID())\n", 577 | " \n", 578 | " np_img3 = sitk.GetArrayFromImage(Original_resampled_label)\n", 579 | " np_img4 = sitk.GetArrayFromImage(label_Original_fixed)\n", 580 | " \n", 581 | " \n", 582 | " print(\"data:\",img_name)\n", 583 | " dice_CSF, dice_GM, dice_WM = dice_similarity(np_img3,np_img4,\"arr\")\n", 584 | " print(\"CSF DICE = {}\".format(dice_CSF), \"GM DICE = {}\".format(dice_GM), \"WM DICE = {}\".format(dice_WM))\n", 585 | " print(\"############################ \")\n", 586 | " \n", 587 | " " 588 | ] 589 | }, 590 | { 591 | "cell_type": "markdown", 592 | "metadata": { 593 | "heading_collapsed": true 594 | }, 595 | "source": [ 596 | "# Creating the excel File of the Registered Training and Validation Path." 597 | ] 598 | }, 599 | { 600 | "cell_type": "code", 601 | "execution_count": 28, 602 | "metadata": { 603 | "hidden": true 604 | }, 605 | "outputs": [ 606 | { 607 | "name": "stdout", 608 | "output_type": "stream", 609 | "text": [ 610 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_01.nii.gz\n", 611 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_01_seg.nii.gz\n", 612 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_01.tfm\n", 613 | "###############################\n", 614 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_03.nii.gz\n", 615 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_03_seg.nii.gz\n", 616 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_03.tfm\n", 617 | "###############################\n", 618 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_04.nii.gz\n", 619 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_04_seg.nii.gz\n", 620 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_04.tfm\n", 621 | "###############################\n", 622 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_05.nii.gz\n", 623 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_05_seg.nii.gz\n", 624 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_05.tfm\n", 625 | "###############################\n", 626 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_06.nii.gz\n", 627 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_06_seg.nii.gz\n", 628 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_06.tfm\n", 629 | "###############################\n", 630 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_07.nii.gz\n", 631 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_07_seg.nii.gz\n", 632 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_07.tfm\n", 633 | "###############################\n", 634 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_08.nii.gz\n", 635 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_08_seg.nii.gz\n", 636 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_08.tfm\n", 637 | "###############################\n", 638 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_09.nii.gz\n", 639 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_09_seg.nii.gz\n", 640 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_09.tfm\n", 641 | "###############################\n", 642 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_16.nii.gz\n", 643 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_16_seg.nii.gz\n", 644 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_16.tfm\n", 645 | "###############################\n", 646 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_18.nii.gz\n", 647 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_18_seg.nii.gz\n", 648 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_18.tfm\n", 649 | "###############################\n", 650 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_11.nii.gz\n", 651 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_11_seg.nii.gz\n", 652 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_11.tfm\n", 653 | "###############################\n", 654 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_12.nii.gz\n", 655 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_12_seg.nii.gz\n", 656 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_12.tfm\n", 657 | "###############################\n", 658 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_13.nii.gz\n", 659 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_13_seg.nii.gz\n", 660 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_13.tfm\n", 661 | "###############################\n", 662 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_14.nii.gz\n", 663 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_14_seg.nii.gz\n", 664 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_14.tfm\n", 665 | "###############################\n", 666 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_data/IBSR_17.nii.gz\n", 667 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_label/IBSR_17_seg.nii.gz\n", 668 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Transformation/IBSR_17.tfm\n", 669 | "###############################\n" 670 | ] 671 | } 672 | ], 673 | "source": [ 674 | "id_list=[]\n", 675 | "sub_t1=[]\n", 676 | "label_list=[]\n", 677 | "tfm_list=[]\n", 678 | "\n", 679 | "\n", 680 | "for im in mylist:\n", 681 | " ###Getting Suvject MRI\n", 682 | " img_fn = str(im[1])\n", 683 | " label_fn=str(im[2])\n", 684 | " img_name = img_fn.split('/')[-1].split('.')[0]\n", 685 | " id_list.append(img_name)\n", 686 | " label_name = label_fn.split('/')[-1].split('.')[0]\n", 687 | " \n", 688 | " ######Getting Path\n", 689 | " reg_img_path=Output_Registered_image_path+img_name+'.nii.gz'\n", 690 | " sub_t1.append(reg_img_path)\n", 691 | " \n", 692 | " reg_label_path=Output_Registered_label_path+label_name+'.nii.gz'\n", 693 | " label_list.append(reg_label_path)\n", 694 | " \n", 695 | " trans_tfm_file_path=Output_Registered_transformation_path+img_name+'.tfm'\n", 696 | " tfm_list.append(trans_tfm_file_path)\n", 697 | " \n", 698 | " \n", 699 | " \n", 700 | " \n", 701 | " print(reg_img_path)\n", 702 | " print(reg_label_path)\n", 703 | " print(trans_tfm_file_path)\n", 704 | " print(\"###############################\")\n", 705 | "\n", 706 | "Inf0_data=pd.DataFrame(list(zip(id_list, sub_t1, label_list,tfm_list)),\n", 707 | "columns=['id','subj_folder','subj_label','registartion_tmf'])\n", 708 | "Inf0_data.to_csv(\"MISAPreorocessingReg_info.csv\", encoding='utf-8', index=False) " 709 | ] 710 | }, 711 | { 712 | "cell_type": "markdown", 713 | "metadata": { 714 | "heading_collapsed": true 715 | }, 716 | "source": [ 717 | "# Registartion For Test Imaes" 718 | ] 719 | }, 720 | { 721 | "cell_type": "code", 722 | "execution_count": 29, 723 | "metadata": { 724 | "hidden": true 725 | }, 726 | "outputs": [ 727 | { 728 | "name": "stdout", 729 | "output_type": "stream", 730 | "text": [ 731 | "['IBSR_02.nii.gz', 'IBSR_10.nii.gz', 'IBSR_15.nii.gz']\n" 732 | ] 733 | } 734 | ], 735 | "source": [ 736 | "Test_data_raw_path=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Test_Data_Raw/\"\n", 737 | "Test_data_list=os.listdir(Test_data_raw_path)\n", 738 | "print(Test_data_list)" 739 | ] 740 | }, 741 | { 742 | "cell_type": "code", 743 | "execution_count": 31, 744 | "metadata": { 745 | "hidden": true 746 | }, 747 | "outputs": [ 748 | { 749 | "name": "stdout", 750 | "output_type": "stream", 751 | "text": [ 752 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Test_Data_Raw/IBSR_02.nii.gz\n" 753 | ] 754 | } 755 | ], 756 | "source": [ 757 | "print(Test_data_raw_path+Test_data_list[0])" 758 | ] 759 | }, 760 | { 761 | "cell_type": "code", 762 | "execution_count": 34, 763 | "metadata": { 764 | "hidden": true, 765 | "scrolled": true 766 | }, 767 | "outputs": [ 768 | { 769 | "data": { 770 | "image/png": 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KWU1NWNuiPvvuC3/7W2gSfeCB0DTUpUv4Lb1Ll/Vv5eWhKfXDD8Ptgw++vn3mGZg3L5xZQDgzadcOysrWv1VUhH69VGDsvHPYJ5KmjJuwzGwb4E533y+7JWVPzs5AIDQl7LZb+M1r1iz9liX1W7kyfIFffjlcckl+3nPVqnCWUlYWzmpEminbl/HW52MgG1OZFKfOneHSS+G110Lzg0h9Fi4M24bOQHKhbdvQ56LwkBxLqwnLzE6vs6sD8CPgxaxXVExOOgkuuwyuvBIOO0xnIbK+1GwI+QwQkTxJtw+k7iDCL4EXgOuzW06Rads2dKKffTZMnQoHHBB3RVJoFCBSwtIKEHfvn+tCitZpp4XJjq68UgEi61OASAlrMECiOaeaFE0tklzl5WEw2AUXhBl7+/SJuyIpJAoQKWGNdaK/B7wbbRu6aaEMgDPPDNe+jx4ddyVSaGpqwqW0ujxWSlCDAeLurdy9dbRt6KbLPCBMQ/HTn4arsWbNirsaKSQ1NeGXC81YICVI/6qz5ZxzwqW9OguR2hYsUPOVlKx0L+NtA5wN9AW6Av+7XrWQpzLJq402gvPOC9NSvPUW7Lhj3BVJIWhsFLpIkUv3DOR64EzgOeA7wEOElQOfzVFdxen888MArquuirsSKRQKEClh6QbIj4BD3f23wOpoewSgy3tr69oVhg8PK63NTvbFaRKpqYFNmly0U6QopRsgHQhrgAAsM7MO7v5v8rkqYLG44IIwhcTYsXFXInFbvTqshaEzEClR6QbI28B3o/tVwGVmdjFhPiypbbPN4Iwz4M474aOP4q5G4rRoUdgqQKREpRsgPwFSixD8DNgD+CEwLBdFFb0LL4S1a7V+etJpEKGUuEYDxMw2AnD3l9z9lej+u+5+kLvv5e4z8lFk0enVC04+GcaPD5dxSjIpQKTENXUG8omZPWhm/2dmGjTYHKNGhRXbrk/2fJOJpgCREtdUgOwJzAHGA/PM7Hoz2zXnVZWCb30rrPb2u999fclQSQ4FiJS4RgPE3V93958DWwKnAN2A583sdTO7wMy656PIonXRRfD553DTTXFXInFIBcjGG8dbh0iOpNWJ7u5r3f0pdx8EbApcC5wLfJjL4oreLrvAD38IN94IK1bEXY3k24IFYRLFNukuuyNSXJo1F5aZbQacA4wENgbuzUVRJeWcc8LlnI88Enclkm8ahS4lrskAMbP2ZjbIzCYD7wMHA2OA7u5+Wq4LLHoDBkDPnnD77XFXIvmmAJES19RlvBOB+cBFwFRgm+gS3knu/lU+Cix6rVqFgYVTpsB//xt3NZJPmsZESlxTZyDLgIPdfQd3/427z23ieKnPaaeFIPnDH+KuRPJJZyBS4pq6Cmu4u7+Yr2JK1hZbwGGHhelNVq2KuxrJh7VrQ9+XAkRKmBaUypehQ+GTT+Bvf4u7EsmHxYtDiChApIQpQPLlsMPCRIvqTE8GDSKUBFCA5EubNqEv5Ikn4EMNnyl5ChBJgLQCxMxOMbNd6uzb1cxOzk1ZJeqMM8L2jjvirUNyTwEiCZDuGcgVrFtQKmUucGV2yylx3/hGGBdyxx2wZk3c1UguKUAkAdINkM7AZ3X2LQU2ym45CTB0KMydC089FXclkkupAOnaNd46RHIo3QB5Cziqzr4jCSsVSnMcfnj4rVSd6aVtwQLYcEMoK4u7EpGcSXeWt5FApZkdB/wX2AY4EDispQWYWQXwJ6AXYer4Y919vfnPzWwrYAJhZmAHDnP3OS19/7wrK4PBg2HcOKiuhh494q5IckGDCCUB0p2NdyawM/AS0BH4J7Czuz+fhRpGAVPcfVtgSvS4PncD17j7DoR1Sop3qb8hQ0IfyF13xV2J5IqmMZEESPsyXnf/0N3HuPs50TZb05oMBCZG9ycCR9Q9wMx2BNq4++Soli+Kei6u7baDvn1hwoQw2ExKj85AJAEaDBAzG1/r/iQzu7u+WxZq2NTdqwGibX2/tm0HfGpmD5vZLDO7pqElds1smJlVmVlVTaojsxANHQqzZ8Ozz8ZdieSCAkQSoLEzkPdr3X+P0PdR361JZvaMmb1Zz21gmnW2AfYDRgDfBbYGBtd3oLuPd/fe7t67WyH/Bz7qKOjSRZ3ppcgdFi5UgEjJa7AT3d2vAoh+058L/NHdl2fyJu5+UEPPmdl8M+vh7tVm1oP6+zY+Ama5++zoZx4B9gaKd3rb8nI45RS45Rb9tlpqPv0UVq/W36mUvCb7QNx9DTAu0/BIw2PAqdH9U4FH6znmJaCLmaX+Rx5AuLS4uA0dGmbnvTsbLYFSMDSIUBIi3U70v5rZD3NUwxhggJm9CwyIHmNmvc1sAvwvxEYAU8zsDcCA4m/72Wkn2Gef0IzlHnc1ki0KEEmIdMeBlAN/NrO/E5qz/vdt5+6ntKQAd19EGFNSd38VMKTW48nALnWPK3pDh8Lpp8PMmbDffnFXI9mgAJGESPcM5E3gN4Rlbet2qEtLHHssdO6szvRSogCRhEj3DOT37v5J3Z1m1j3L9SRPx45w4olhUOHNN4cwkeK2ILoORAEiJS7dM5B3Gthf/B3ZheDoo2H58tCMJcWvpgY6dQpX2omUsHQDxNbbYdYZ0DDqbNhnH2jbFqZNi7sSyQZNYyIJ0WgTlpmlOszbm1ndZfQ2Bu7LVWGJ0qED7LWXAqRUaFyPJERTfSCDCGcflUDt1QcdmO/u/8lVYYnTvz+MHg2ffaZ+kGJXUwNbbBF3FSI512gTlrtPd/dpQNfofur2nMIjy/r1CxMrzpgRdyXSUjoDkYRItw9kjZmNNrPZZrYUwMwONrNzc1hbsuy9d1grRM1Yxc1dASKJkW6A3EBYD+Qk1g0i/BdwVi6KSiT1g5SGzz+HlSsVIJII6QbIEcCJ7v53oiuv3P1jYPNcFZZI/fvDK6/A0qVxVyKZ0iBCSZB0A2QldTrco4kNF2W9oiRL9YNoPEjxUoBIgqQbIA8CE83sGwDRtOs3A/fnqrBESvWDTJ0adyWSKQWIJEi6AfJLYA7wBrAR8C4wD/h1bspKqPbtQ4ioH6R4KUAkQdIKEHdf6e7nu3snYFNgA3f/qbuvzG15CdS/P8yaFRYlkuKjebAkQRoNEDPbqu4NaA9sWeuxZJP6QYpbTU24oq5jx7grEcm5pkaiz2HdZbvrzYcVPdc6mwUl3t57Q7t2oR/kBz+IuxppLo0BkQRpqgnrdUJ/x8VAT6BtnVtZTqtLovJy9YMUMwWIJEhTU5nsBhwNVAAzCXNiHQ+UufuaaKlZybZ+/dQPUqwUIJIgTXaiu/ub7v5z4BvAOOAHQLWZ7ZHr4hKrf/8wJYbmxSo+ChBJkHQv4wXYFugL7APMApbkpCIJU5q0a6dmrGKkAJEEaWo9kArgBOBUYANgErC/u9ddG0Syqbw8LDKlAYXF5csvYdkyBYgkRlNXYc0D3icEx4vRvm3MbJvUAe7+bI5qS7Z+/eDXv4YlS6BLl7irkXRoEKEkTFMB8glQDgyNbnU5sHW2ixJCP8hll4V+kMMPj7saSYcCRBKm0QBx9155qkPq2nPP0JQ1bZoCpFgoQCRhmtOJLvmU6gdRR3rxSE1jsskm8dYhkicKkELWrx+8+iosXhx3JZIOnYFIwihACpnGgxSXmppw+XWnTnFXIpIXCpBCVrsfRApfagyI1TdtnEjpUYAUsnbtoE8fBUix0CBCSRgFSKHr1w9ee039IMVAASIJowApdP36hX6Q556LuxJpigJEEkYBUuj23DMsdatmrMKnAJGEUYAUOvWDFIfly+GLLxQgkigKkGLQrx+8/rr6QQqZxoBIAsUeIGZWYWaTzezdaLvezIFm1t/MXq11W25mR8RRbyxS/SDTp8ddiTREASIJFHuAAKOAKe6+LTAlevw17j7V3XeLVkg8APgKeDq/ZcZI/SCFLxUgmsZEEqQQAmQgMDG6PxFo6sziaOAJd/8qp1UVkrIy2HdfBUghS82DpTMQSZBCCJBN3b0aINo29Svc8cB9DT1pZsPMrMrMqmpSvxWWglQ/yKJFcVci9VETliRQXgLEzJ4xszfruQ1s5uv0AL4NPNXQMe4+3t17u3vvbqX0n/mAA8J2/Ph465D61dRA27aw4YZxVyKSN00tKJUV7n5QQ8+Z2Xwz6+Hu1VFALGjkpY4F/uLuq7JeZKHbe2845hi45BLYf//QpCWFo6YGunbVPFiSKIXQhPUYYc11ou2jjRx7Ao00X5U0M7j9dujVC447DhYujLsiqU2DCCWBCiFAxgADzOxdYED0GDPrbWYTUgeZWS9gSyC517JuuCE88ED4sjr5ZFi7Nu6KJEUBIgkUe4C4+yJ3P9Ddt422i6P9Ve4+pNZxc9x9c3dP9rfmHnvADTfAk0/C2LFxVyMpChBJoNgDRDIwfHhoxrr4Yk2yWCgUIJJACpBiZBauxvrmN+GEE9aNQZB4rFwJS5cqQCRxFCDFqnNnePDBMC5E/SHxSl3QoACRhFGAFLNdd4Ubb4Snn4bf/CbuapJL05hIQilAit3QoXDiifCrX8HUqXFXk0yaxkQSSgFS7Mzg97+HbbcNQTJ/ftwVJY+mMZGEUoCUgk6dQn/I0qVw0kmwZk3cFSWLAkQSSgFSKr79bbj5ZpgyBa67Lu5qkqWmBlq3hi7rLWUjUtIUIKXktNPg8MPhyit1aW8+1dTAxhtDK/13kmTRv/hSYgbXXAPLloVOdckPDSKUhFKAlJrttgsj1cePh7feiruaZFCASEIpQErRr34FG2wAF14YdyXJoACRhFKAlKKuXeGii+Bvfwud6pJbChBJKAVIqTrvPOjZEy64QJf15tLq1bB4sQJEEkkBUqrKy2HMGHjtNZg0Ke5qSldqjXpNYyIJpAApZccdB3vtFZqzvvwy7mpKkwYRSoIpQEqZWRhUOG+eBhfmiubBkgRTgJS6ffeFo46Cq6+G6uq4qyk9OgORBFOAJMHYsWHRo0svjbuS0qMAkQRTgCTBN78J554Ld9wBb7wRdzWlpaYmNBVuvHHclYjknQIkKS6+GDbcEEaMiLuS0lJTAxUVYTJFkYRRgCRFRQVccklYvfCpp+KupnQsWKDmK0ksBUiSnHNOaM4aMUKDC1uqujrMfvzww7DTTnFXIxKLNnEXIHlUVhYGFx5zDPz0p2ENkVatwq1163X3U4832gj23jvMqyXB8uVw/fVhDfoVK0IYX3xx3FWJxEIBkjRHHQWHHAI33ZTe8a1bQ+/e0K9fuO27bzIDxT2cbfz85/D++zBwIFx7LWyzTdyVicRGAZI0ZlBZGdZOX7s23Nasqf/+vHkwfTpMmwbjxoXLgZMYKK++CuefHz6LnXeGZ56BAw+MuyqR2Jm7x11DzvTu3durqqriLqM0fPkl/P3vIUymTYN//hNWrQrNYj/+8bqrvErJggXhzzVhQrgI4corYcgQaKPfu6S0mdnL7t67yeMUIJKRVKDccw/cfXcYB3H55TB0aGl8wf7pT2Fhri++CDMbX3pp6BMSSYB0A0RXYUlmOnaEgw6Cu+6CqirYcUc4+2zYbbfivkx46VI4+WQ4/nj41rfCwMtx4xQeIvVQgEjL7bFHaNZ66KGwHvv3vw+HHQZvvx13Zc0zYwbsuivcdx9cdhnMnAnbbx93VSIFSwEi2WEGP/pRWIf9mmvg+efDZcLnngsLF8ZdXeNWroRf/hL69g3NbzNnhmWBS6EpTiSHFCCSXe3ahbER770Hw4bBrbeGS10ffTTuyur3739Dnz5w1VVw+ukwa1YY+yIiTVKASG506wa33AKvvw7bbQdHHw0PPhh3Veu4h3DbYw+YMyeM8ZgwofQvSRbJIp2jS27ttBNMmRL6RI4/PozeHjQonlqWLoU33wyh9sgjYV6wQw6BO++EHj3iqUmkiMUeIGZWAfwJ6AXMAY519yX1HHc18H+Es6bJwE+8lK9BLiUbbABPPgmHHw6nnBL6HE4/PXfvt2oVvPNOCIo33gi311+HDz9cd0yXLvDb34Y+mlY6ERfJROwBAowCprj7GDMbFT0eWfsAM+sD7AvsEu2aCfQFpuWxTmmJjh3h8cfhyCPhjDNCiAwfnv7Pu8PUqWE0+OefN35bsiSECISO8O23DyPmhw+HXXYJnftbbhk6/kUkY4Xsu3QjAAAJ5ElEQVQQIAOBftH9iYRQGFnnGAfKgTLAgLbA/PyUJ1nTvn1oOjr2WDjrrNCc9ZOfNP1zU6eGq6JmzAiPO3UKZzW1b1tuue5+RUVoOvv2t0N4lJXl9s8lklCFECCbuns1gLtXm9kmdQ9w97+b2VSgmhAgN7t7vYMMzGwYMAxgq622yl3Vkpnycvjzn+HEE8P8UitWwIUX1n/s9OkhOKZPh802CxNADhkSXkNEYpeXADGzZ4Du9Tx1UZo/vw2wA7BFtGuyme3v7s/VPdbdxwPjIUxlklnFklNlZXD//aE/ZOTIECKXXLLu+RkzQnBMnQrdu4e+imHDFBwiBSYvAeLuBzX0nJnNN7Me0dlHD2BBPYcdCbzo7l9EP/MEsDewXoBIkWjTBiZNCuNGLr00hMihh4bgmDIFNt00rLtx5pmh6UtECk4hXH7yGHBqdP9UoL4RZx8Cfc2sjZm1JXSgF9k8GbKe1q3hD38IZxejR8P3vheumLruOpg9OzRxKTxEClYh9IGMAR4wszMIQXEMgJn1Boa7+xDgz8ABwBuEDvUn3f2vMdUr2dSqFdx2G2y9NbRtG844OnaMuyoRSYOmcxcRka/RdO4iIpJTChAREcmIAkRERDKiABERkYwoQEREJCMKEBERyYgCREREMqIAERGRjJT0QEIzqwE+aMFLdAUWZqmcfFLd+aW680t1515Pd+/W1EElHSAtZWZV6YzGLDSqO79Ud36p7sKhJiwREcmIAkRERDKiAGnc+LgLyJDqzi/VnV+qu0CoD0RERDKiMxAREcmIAqQeZvZ9M/uPmb1nZqPiriddZjbHzN4ws1fNrKAXQjGzO8xsgZm9WWtfhZlNNrN3o22XOGusTwN1X2ZmH0ef+6tmdlicNdbHzLY0s6lm9raZ/cvMfhLtL+jPvJG6C/ozN7NyM/unmb0W1f3raP83zOwf0ef9JzMri7vWllATVh1m1hp4BxgAfAS8BJzg7m/FWlgazGwO0NvdC/5aczPbH/gCuNvdd472XQ0sdvcxUXB3cfeRcdZZVwN1XwZ84e7XxllbY8ysB9DD3V8xsw2Al4EjgMEU8GfeSN3HUsCfuZkZ0NHdv4iW4Z4J/AT4GfCwu99vZrcBr7n7rXHW2hI6A1nfnsB77j7b3VcC9wMDY66p5Lj7c8DiOrsHAhOj+xMJXxQFpYG6C567V7v7K9H9z4G3gc0p8M+8kboLmgdfRA/bRjcnLM3952h/wX3ezaUAWd/mwNxajz+iCP7BRhx42sxeNrNhcReTgU3dvRrCFwewScz1NMe5ZvZ61MRVUM1AdZlZL2B34B8U0Wdep24o8M/czFqb2avAAmAy8F/gU3dfHR1STN8t9VKArM/q2Vcs7Xz7uvsewKHAOVFzi+TercA3gd2AauC6eMtpmJl1Ah4Cznf3z+KuJ1311F3wn7m7r3H33YAtCC0bO9R3WH6ryi4FyPo+Aras9XgLYF5MtTSLu8+LtguAvxD+0RaT+VGbd6rte0HM9aTF3edHXxZrgdsp0M89aot/CLjX3R+Odhf8Z15f3cXymQO4+6fANGBvYCMzaxM9VTTfLQ1RgKzvJWDb6GqJMuB44LGYa2qSmXWMOhkxs47AwcCbjf9UwXkMODW6fyrwaIy1pC31BRw5kgL83KNO3T8Ab7v7uFpPFfRn3lDdhf6Zm1k3M9sout8eOIjQfzMVODo6rOA+7+bSVVj1iC4JvAFoDdzh7qNjLqlJZrY14awDoA3wx0Ku28zuA/oRZiidD/wKeAR4ANgK+BA4xt0LqsO6gbr7EZpSHJgDnJnqVygUZvY9YAbwBrA22v1LQn9CwX7mjdR9AgX8mZvZLoRO8taEX9QfcPfLo/+n9wMVwCxgkLuviK/SllGAiIhIRtSEJSIiGVGAiIhIRhQgIiKSEQWIiIhkRAEiIiIZUYCINJOZfRFdjlnUzOwuM7sy7jqkeClApKhEU9YfFN0fbGYzc/x+08xsSO197t7J3Wfn4L3mmNn8aCBoat8QM5uW7fcSyQYFiCRWrSklCkkbwrTfRSVaBkESRgEiRcnMdgBuA/aJmpQ+jfa3M7NrzezD6Lf526KpJDCzfmb2kZmNNLNPgDvNrIuZPW5mNWa2JLq/RXT8aGA/4OboPW6O9ruZbRPd39DM7o5+/gMzu9jMWkXPDTazmVE9S8zsfTM7tIk/2jXAiNQ0GHX+zL2i925Ta9//zpCi93vezK43s0/NbLaZ9Yn2z7WwENapdV62q4WFpD43s+lm1rPWa28fPbfYwgJrx9Z67i4zu9XMKs3sS6B/Gn9tUmIUIFKU3P1tYDjw96hJKfWFOxbYjjDNxTaE6bIvrfWj3QnTSPQEhhH+D9wZPd4KWAbcHL3HRYRpNM6N3uPcekq5CdgQ2BroC5wCnFbr+b2A/xCmPrka+EM0v1NDqggT741o8kOo317A68DGwB8J02Z8l/BZDCKEYadax58EXBHV9ypwL/xvPrXJ0WtsQpg65BYz26nWz54IjAY2ICyYJAmjAJGSEX0xDwV+6u6LowWIfkOYEDNlLfArd1/h7svcfZG7P+TuX0XHjyYEQTrv1xo4DviFu3/u7nMI04qfXOuwD9z9dndfQ5gbqQewaRMvfSlwnpl1S6eOOt539zuj9/sTYWbpy6M/79PASkKYpPzN3Z+L5mO6iHBGtyXwA2BO9Fqro0WdHmLdRIAAj7r78+6+1t2XZ1CrFLlCbAMWyVQ3oAPwcq1f8o0woV1KTe0vOzPrAFwPfB9ILUq0gZm1jr6EG9MVKAM+qLXvA76+SNAnqTvu/lVUV+0zgPW4+5tm9jgwijCDa3PMr3V/WfR6dffVfv//LZ4WLb+6GNiMcEa2V6ppMNIGmFTfz0oyKUCkmNWdCXQh4QtyJ3f/OM2fuQD4FrCXu39iZrsRZkm1Bo6v+36rCF+2b0X7tgIaeu/m+BXwCl9fKOnLaNsBSC0G1b2F7/O/tW+ipq0KwhoVc4Hp7j6gkZ/VTKwJpyYsKWbzgS2idVuotbjQ9Wa2CYCZbW5mhzTyGhsQQudTM6sgfHHXfY96x3xEZygPAKPNbIOoA/pnwD0t+DOlXvs9QhPUj2vtqyGE0yALy6WeTliVryUOM7PvRZ/hFcA/3H0u8DiwnZmdbGZto9t3o4sXRAAFiBS3Z4F/AZ+Y2cJo30jgPeBFM/sMeIZwhtGQG4D2hLOJF4En6zz/W+Do6CqqG+v5+fMIZwazCR3JfwTuyOyPs57LgY519g0Ffg4sAnYCXmjhe/yREJqLge8QOtWJ+oMOJvQfzSM0xY0F2rXw/aSEaD0QERHJiM5AREQkIwoQERHJiAJEREQyogAREZGMKEBERCQjChAREcmIAkRERDKiABERkYwoQEREJCP/DwT77E68a5slAAAAAElFTkSuQmCC\n", 771 | "text/plain": [ 772 | "
" 773 | ] 774 | }, 775 | "metadata": {}, 776 | "output_type": "display_data" 777 | }, 778 | { 779 | "name": "stdout", 780 | "output_type": "stream", 781 | "text": [ 782 | "Final metric value: -0.3417778050822575\n", 783 | "Optimizer's stopping condition, GradientDescentOptimizerv4Template: Convergence checker passed at iteration 9.\n" 784 | ] 785 | }, 786 | { 787 | "data": { 788 | "application/vnd.jupyter.widget-view+json": { 789 | "model_id": "0222ebe7be7747e8a8a6dfbbf52fde65", 790 | "version_major": 2, 791 | "version_minor": 0 792 | }, 793 | "text/plain": [ 794 | "interactive(children=(IntSlider(value=91, description='image_z', max=182), FloatSlider(value=0.5, description=…" 795 | ] 796 | }, 797 | "metadata": {}, 798 | "output_type": "display_data" 799 | } 800 | ], 801 | "source": [ 802 | "REegistered_Test_output_path=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata/\"\n", 803 | "Registered_Testdata_tmf_file=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/\"\n", 804 | "\n", 805 | "fixed_image = sitk.ReadImage('MNI152_T1_1mm_Brain.nii.gz', sitk.sitkFloat32)\n", 806 | "\n", 807 | "for im in range(0,3):\n", 808 | " \n", 809 | " complete_test_img_path=Test_data_raw_path+Test_data_list[im]\n", 810 | " \n", 811 | " ###Getting Suvject MRI\n", 812 | " img_fn = str(complete_test_img_path)\n", 813 | " img_name = img_fn.split('/')[-1].split('.')[0]\n", 814 | " \n", 815 | " print(img_name)\n", 816 | " \n", 817 | " ########### Output Name & Folder ##########\n", 818 | " Registered_imageName=img_name+'.nii.gz'\n", 819 | " Transformation_imageName=img_name+'.tfm'\n", 820 | " \n", 821 | " # =============================================================================\n", 822 | " # Loading the data\n", 823 | " # =============================================================================\n", 824 | " moving_image = sitk.ReadImage(img_fn, sitk.sitkFloat32)\n", 825 | " interact(display_images, fixed_image_z=(0,fixed_image.GetSize()[2]-1), moving_image_z=(0,moving_image.GetSize()[2]-1), fixed_npa = fixed(sitk.GetArrayViewFromImage(fixed_image)), moving_npa=fixed(sitk.GetArrayViewFromImage(moving_image)));\n", 826 | " # =============================================================================\n", 827 | " # initial Alighment \n", 828 | " # =============================================================================\n", 829 | " initial_transform = sitk.CenteredTransformInitializer(fixed_image, \n", 830 | " moving_image, \n", 831 | " sitk.Euler3DTransform(), \n", 832 | " sitk.CenteredTransformInitializerFilter.GEOMETRY)\n", 833 | "\n", 834 | " moving_resampled = sitk.Resample(moving_image, fixed_image, initial_transform, sitk.sitkNearestNeighbor, 0.0, moving_image.GetPixelID())\n", 835 | "\n", 836 | " interact(display_images_with_alpha, image_z=(0,fixed_image.GetSize()[2]), alpha=(0.0,1.0,0.05), fixed = fixed(fixed_image), moving=fixed(moving_resampled));\n", 837 | " # =============================================================================\n", 838 | " # Registration\n", 839 | " # =============================================================================\n", 840 | " registration_method = sitk.ImageRegistrationMethod()\n", 841 | "\n", 842 | " # Similarity metric settings.\n", 843 | " registration_method.SetMetricAsMattesMutualInformation(numberOfHistogramBins=50)\n", 844 | " registration_method.SetMetricSamplingStrategy(registration_method.RANDOM)\n", 845 | " registration_method.SetMetricSamplingPercentage(0.01)\n", 846 | "\n", 847 | " registration_method.SetInterpolator(sitk.sitkNearestNeighbor)\n", 848 | "\n", 849 | " # Optimizer settings.\n", 850 | " registration_method.SetOptimizerAsGradientDescent(learningRate=1.0, numberOfIterations=100, convergenceMinimumValue=1e-6, convergenceWindowSize=10)\n", 851 | " registration_method.SetOptimizerScalesFromPhysicalShift()\n", 852 | "\n", 853 | " # Setup for the multi-resolution framework. \n", 854 | " registration_method.SetShrinkFactorsPerLevel(shrinkFactors = [4,2,1])\n", 855 | " registration_method.SetSmoothingSigmasPerLevel(smoothingSigmas=[2,1,0])\n", 856 | " registration_method.SmoothingSigmasAreSpecifiedInPhysicalUnitsOn()\n", 857 | "\n", 858 | " # Don't optimize in-place, we would possibly like to run this cell multiple times.\n", 859 | " registration_method.SetInitialTransform(initial_transform, inPlace=False)\n", 860 | "\n", 861 | " # Connect all of the observers so that we can perform plotting during registration.\n", 862 | " registration_method.AddCommand(sitk.sitkStartEvent, start_plot)\n", 863 | " registration_method.AddCommand(sitk.sitkEndEvent, end_plot)\n", 864 | " registration_method.AddCommand(sitk.sitkMultiResolutionIterationEvent, update_multires_iterations) \n", 865 | " registration_method.AddCommand(sitk.sitkIterationEvent, lambda: plot_values(registration_method))\n", 866 | "\n", 867 | " final_transform = registration_method.Execute(sitk.Cast(fixed_image, sitk.sitkFloat32), \n", 868 | " sitk.Cast(moving_image, sitk.sitkFloat32))\n", 869 | "\n", 870 | " # =============================================================================\n", 871 | " # post processing Analysis\n", 872 | " # =============================================================================\n", 873 | " print('Final metric value: {0}'.format(registration_method.GetMetricValue()))\n", 874 | " print('Optimizer\\'s stopping condition, {0}'.format(registration_method.GetOptimizerStopConditionDescription()))\n", 875 | " #Visualize Expected Results\n", 876 | "\n", 877 | " moving_resampled = sitk.Resample(moving_image, fixed_image, final_transform, sitk.sitkNearestNeighbor, 0.0, moving_image.GetPixelID())\n", 878 | "\n", 879 | " interact(display_images_with_alpha, image_z=(0,fixed_image.GetSize()[2]), alpha=(0.0,1.0,0.05), fixed = fixed(fixed_image), moving=fixed(moving_resampled));\n", 880 | "\n", 881 | " sitk.WriteImage(moving_resampled, os.path.join(REegistered_Test_output_path, Registered_imageName))\n", 882 | " sitk.WriteTransform(final_transform,os.path.join(Registered_Testdata_tmf_file,Transformation_imageName))" 883 | ] 884 | }, 885 | { 886 | "cell_type": "markdown", 887 | "metadata": { 888 | "heading_collapsed": true 889 | }, 890 | "source": [ 891 | "# Creating A Excel File For Registered Test Data" 892 | ] 893 | }, 894 | { 895 | "cell_type": "code", 896 | "execution_count": 35, 897 | "metadata": { 898 | "hidden": true 899 | }, 900 | "outputs": [ 901 | { 902 | "name": "stdout", 903 | "output_type": "stream", 904 | "text": [ 905 | "IBSR_02\n", 906 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata/IBSR_02.nii.gz\n", 907 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_02.tfm\n", 908 | "###############################\n", 909 | "IBSR_10\n", 910 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata/IBSR_10.nii.gz\n", 911 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_10.tfm\n", 912 | "###############################\n", 913 | "IBSR_15\n", 914 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata/IBSR_15.nii.gz\n", 915 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_15.tfm\n", 916 | "###############################\n" 917 | ] 918 | } 919 | ], 920 | "source": [ 921 | "test_id_list=[]\n", 922 | "test_sub_t1=[]\n", 923 | "test_tfm_list=[]\n", 924 | "\n", 925 | "\n", 926 | "REegistered_Test_output_path=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata/\"\n", 927 | "Registered_Testdata_tmf_file=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/\"\n", 928 | "\n", 929 | "fixed_image = sitk.ReadImage('MNI152_T1_1mm_Brain.nii.gz', sitk.sitkFloat32)\n", 930 | "\n", 931 | "for im in range(0,3):\n", 932 | " \n", 933 | " complete_test_img_path=Test_data_raw_path+Test_data_list[im]\n", 934 | " \n", 935 | " ###Getting Suvject MRI\n", 936 | " img_fn = str(complete_test_img_path)\n", 937 | " img_name = img_fn.split('/')[-1].split('.')[0]\n", 938 | " print(img_name)\n", 939 | " test_id_list.append(img_name)\n", 940 | " \n", 941 | "\n", 942 | " ######Getting Path\n", 943 | " reg_img_path=REegistered_Test_output_path+img_name+'.nii.gz'\n", 944 | " test_sub_t1.append(reg_img_path)\n", 945 | " \n", 946 | " \n", 947 | " trans_tfm_file_path=Registered_Testdata_tmf_file+img_name+'.tfm'\n", 948 | " test_tfm_list.append(trans_tfm_file_path)\n", 949 | " \n", 950 | " \n", 951 | " \n", 952 | " \n", 953 | " print(reg_img_path)\n", 954 | " print(trans_tfm_file_path)\n", 955 | " print(\"###############################\")\n", 956 | "\n", 957 | "Inf0_data=pd.DataFrame(list(zip(test_id_list, test_sub_t1, test_tfm_list)),\n", 958 | "columns=['id','subj_folder','registartion_tmf'])\n", 959 | "Inf0_data.to_csv(\"MISAPreorocessingTestReg_info.csv\", encoding='utf-8', index=False)" 960 | ] 961 | }, 962 | { 963 | "cell_type": "code", 964 | "execution_count": null, 965 | "metadata": { 966 | "hidden": true 967 | }, 968 | "outputs": [], 969 | "source": [] 970 | } 971 | ], 972 | "metadata": { 973 | "kernelspec": { 974 | "display_name": "Python 3", 975 | "language": "python", 976 | "name": "python3" 977 | }, 978 | "language_info": { 979 | "codemirror_mode": { 980 | "name": "ipython", 981 | "version": 3 982 | }, 983 | "file_extension": ".py", 984 | "mimetype": "text/x-python", 985 | "name": "python", 986 | "nbconvert_exporter": "python", 987 | "pygments_lexer": "ipython3", 988 | "version": "3.6.7" 989 | }, 990 | "toc": { 991 | "base_numbering": 1, 992 | "nav_menu": {}, 993 | "number_sections": true, 994 | "sideBar": true, 995 | "skip_h1_title": false, 996 | "title_cell": "Table of Contents", 997 | "title_sidebar": "Contents", 998 | "toc_cell": true, 999 | "toc_position": {}, 1000 | "toc_section_display": true, 1001 | "toc_window_display": true 1002 | }, 1003 | "varInspector": { 1004 | "cols": { 1005 | "lenName": 16, 1006 | "lenType": 16, 1007 | "lenVar": 40 1008 | }, 1009 | "kernels_config": { 1010 | "python": { 1011 | "delete_cmd_postfix": "", 1012 | "delete_cmd_prefix": "del ", 1013 | "library": "var_list.py", 1014 | "varRefreshCmd": "print(var_dic_list())" 1015 | }, 1016 | "r": { 1017 | "delete_cmd_postfix": ") ", 1018 | "delete_cmd_prefix": "rm(", 1019 | "library": "var_list.r", 1020 | "varRefreshCmd": "cat(var_dic_list()) " 1021 | } 1022 | }, 1023 | "types_to_exclude": [ 1024 | "module", 1025 | "function", 1026 | "builtin_function_or_method", 1027 | "instance", 1028 | "_Feature" 1029 | ], 1030 | "window_display": false 1031 | } 1032 | }, 1033 | "nbformat": 4, 1034 | "nbformat_minor": 2 1035 | } 1036 | -------------------------------------------------------------------------------- /MISA_Project_Report.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fitushar/Brain-Tissue-Segmentation-Using-Deep-Learning-Pipeline-NeuroNet/e3d33136d34a9ea4b55b3e210c78271980b683e2/MISA_Project_Report.pdf -------------------------------------------------------------------------------- /PreparingTestingData.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "toc": true 7 | }, 8 | "source": [ 9 | "

Table of Contents

\n", 10 | "
" 11 | ] 12 | }, 13 | { 14 | "cell_type": "markdown", 15 | "metadata": {}, 16 | "source": [ 17 | " Description & Instructions How to use this Script.\n", 18 | "\n", 19 | "* 1) Import the libraries running the Section \"Importing Libararies\"\n", 20 | "* 2) Function used in for histpgram matching and Normalization\n", 21 | "* 3) Apply the Histogram Maching\n", 22 | "* 4) Making 1mx1mx1m predicted Segmented Prediction to Original Spacing Using the prevviously saved Transformation Matrix" 23 | ] 24 | }, 25 | { 26 | "cell_type": "markdown", 27 | "metadata": { 28 | "heading_collapsed": true 29 | }, 30 | "source": [ 31 | "# Importing Libraries" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 1, 37 | "metadata": { 38 | "hidden": true 39 | }, 40 | "outputs": [ 41 | { 42 | "name": "stderr", 43 | "output_type": "stream", 44 | "text": [ 45 | "C:\\Users\\Fakrul-IslamTUSHAR\\Anaconda2\\envs\\nnet\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", 46 | " from ._conv import register_converters as _register_converters\n" 47 | ] 48 | } 49 | ], 50 | "source": [ 51 | "import SimpleITK as sitk\n", 52 | "import os\n", 53 | "import pandas as pd\n", 54 | "import numpy as np\n", 55 | "import glob\n", 56 | "import os\n", 57 | "import nibabel as nib\n", 58 | "\n", 59 | "from matplotlib import pyplot as plt\n", 60 | "from dltk.io.augmentation import *\n", 61 | "from dltk.io.preprocessing import *" 62 | ] 63 | }, 64 | { 65 | "cell_type": "markdown", 66 | "metadata": { 67 | "heading_collapsed": true 68 | }, 69 | "source": [ 70 | "# Function" 71 | ] 72 | }, 73 | { 74 | "cell_type": "markdown", 75 | "metadata": {}, 76 | "source": [ 77 | "# Histogram Matching" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": 2, 83 | "metadata": {}, 84 | "outputs": [], 85 | "source": [ 86 | "# -------------------------------------------------------\n", 87 | "##This functions were coppied from the github reprocesoty:https://github.com/sergivalverde/mri_utils\n", 88 | "# if you this code please refer this github.\n", 89 | "\n", 90 | "# Image processing functions\n", 91 | "# Useful for brain MRI analysis\n", 92 | "#\n", 93 | "# Sergi Valverde 2018\n", 94 | "# svalverde@eia.udg.edu\n", 95 | "#\n", 96 | "# -------------------------------------------------------\n", 97 | "\n", 98 | "import numpy as np\n", 99 | "from scipy.ndimage import label\n", 100 | "from scipy.ndimage import labeled_comprehension as lc\n", 101 | "import SimpleITK as sitk\n", 102 | "\n", 103 | "\n", 104 | "\n", 105 | "def histogram_matching(mov_scan, ref_scan,\n", 106 | " histogram_levels=2048,\n", 107 | " match_points=100,\n", 108 | " set_th_mean=True):\n", 109 | " \"\"\"\n", 110 | " Histogram matching following the method developed on\n", 111 | " Nyul et al 2001 (ITK implementation)\n", 112 | " inputs:\n", 113 | " - mov_scan: np.array containing the image to normalize\n", 114 | " - ref_scan np.array containing the reference image\n", 115 | " - histogram levels\n", 116 | " - number of matched points\n", 117 | " - Threshold Mean setting\n", 118 | " outputs:\n", 119 | " - histogram matched image\n", 120 | " \"\"\"\n", 121 | "\n", 122 | " # convert np arrays into itk image objects\n", 123 | " ref = sitk.GetImageFromArray(ref_scan.astype('float32'))\n", 124 | " mov = sitk.GetImageFromArray(mov_scan.astype('float32'))\n", 125 | "\n", 126 | " # perform histogram matching\n", 127 | " caster = sitk.CastImageFilter()\n", 128 | " caster.SetOutputPixelType(ref.GetPixelID())\n", 129 | "\n", 130 | " matcher = sitk.HistogramMatchingImageFilter()\n", 131 | " matcher.SetNumberOfHistogramLevels(histogram_levels)\n", 132 | " matcher.SetNumberOfMatchPoints(match_points)\n", 133 | " matcher.SetThresholdAtMeanIntensity(set_th_mean)\n", 134 | " matched_vol = matcher.Execute(mov, ref)\n", 135 | "\n", 136 | " return matched_vol" 137 | ] 138 | }, 139 | { 140 | "cell_type": "markdown", 141 | "metadata": {}, 142 | "source": [ 143 | "# Normalization and Histogram Matching" 144 | ] 145 | }, 146 | { 147 | "cell_type": "markdown", 148 | "metadata": {}, 149 | "source": [ 150 | "## Loading Reference data " 151 | ] 152 | }, 153 | { 154 | "cell_type": "code", 155 | "execution_count": 4, 156 | "metadata": {}, 157 | "outputs": [ 158 | { 159 | "name": "stderr", 160 | "output_type": "stream", 161 | "text": [ 162 | "C:\\Users\\Fakrul-IslamTUSHAR\\Anaconda2\\envs\\nnet\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n", 163 | " \"\"\"\n" 164 | ] 165 | } 166 | ], 167 | "source": [ 168 | "mylist = pd.read_csv(\n", 169 | " \"MISAPreorocessingTestReg_info.csv\",\n", 170 | " dtype=object,\n", 171 | " keep_default_na=False,\n", 172 | " na_values=[]).as_matrix()" 173 | ] 174 | }, 175 | { 176 | "cell_type": "code", 177 | "execution_count": 5, 178 | "metadata": {}, 179 | "outputs": [], 180 | "source": [ 181 | "Save_Preprocessed_Test_data=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/save_processed_test_data/\"\n" 182 | ] 183 | }, 184 | { 185 | "cell_type": "code", 186 | "execution_count": 6, 187 | "metadata": {}, 188 | "outputs": [], 189 | "source": [ 190 | "ref_volume=sitk.ReadImage(\"ref_07.nii.gz\", sitk.sitkFloat32)\n", 191 | "ref_array = sitk.GetArrayFromImage(ref_volume)" 192 | ] 193 | }, 194 | { 195 | "cell_type": "code", 196 | "execution_count": 8, 197 | "metadata": {}, 198 | "outputs": [ 199 | { 200 | "name": "stdout", 201 | "output_type": "stream", 202 | "text": [ 203 | "IBSR_02\n", 204 | "########Saved#########\n", 205 | "IBSR_10\n", 206 | "########Saved#########\n", 207 | "IBSR_15\n", 208 | "########Saved#########\n" 209 | ] 210 | } 211 | ], 212 | "source": [ 213 | "for im in mylist:\n", 214 | " ###Getting Suvject MRI\n", 215 | " img_fn = str(im[1])\n", 216 | " img_name = img_fn.split('/')[-1].split('.')[0]\n", 217 | " print(img_name)\n", 218 | " #Image_Name.append(img_name)\n", 219 | " histoMached_imageName=img_name+'.nii.gz' \n", 220 | "\n", 221 | "# =============================================================================\n", 222 | "# load data\n", 223 | "# =============================================================================\n", 224 | " #Loading the image\n", 225 | " sitk_t1 = sitk.ReadImage(img_fn, sitk.sitkFloat32)\n", 226 | " t1 = sitk.GetArrayFromImage(sitk_t1)\n", 227 | " normalized_vol=normalise_zero_one(t1)\n", 228 | " \n", 229 | " \n", 230 | " Histo_mached_vol=histogram_matching(normalized_vol,ref_array)\n", 231 | " Histo_mached_vol.CopyInformation(sitk_t1)\n", 232 | " \n", 233 | " sitk.WriteImage(Histo_mached_vol, os.path.join(Save_Preprocessed_Test_data,histoMached_imageName))\n", 234 | " print(\"########Saved#########\")" 235 | ] 236 | }, 237 | { 238 | "cell_type": "code", 239 | "execution_count": 9, 240 | "metadata": {}, 241 | "outputs": [ 242 | { 243 | "name": "stdout", 244 | "output_type": "stream", 245 | "text": [ 246 | "[['IBSR_02'\n", 247 | " 'C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata/IBSR_02.nii.gz'\n", 248 | " 'C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_02.tfm']\n", 249 | " ['IBSR_10'\n", 250 | " 'C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata/IBSR_10.nii.gz'\n", 251 | " 'C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_10.tfm']\n", 252 | " ['IBSR_15'\n", 253 | " 'C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata/IBSR_15.nii.gz'\n", 254 | " 'C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_15.tfm']]\n" 255 | ] 256 | } 257 | ], 258 | "source": [ 259 | "print(mylist)" 260 | ] 261 | }, 262 | { 263 | "cell_type": "markdown", 264 | "metadata": { 265 | "heading_collapsed": true 266 | }, 267 | "source": [ 268 | "# Making the labels back to original Spacing" 269 | ] 270 | }, 271 | { 272 | "cell_type": "markdown", 273 | "metadata": { 274 | "hidden": true 275 | }, 276 | "source": [ 277 | "For This Process Following steps need to be done.\n", 278 | "\n", 279 | "* 1) Put path of the Prediction(Segmented Nifti file) in Section 5.1 \"Final_Seg_test_path\"\n", 280 | "* 2) Put path of the RAW DATA(Nifti file) in Section 5.2 \"Test_data_raw_path\"\n", 281 | "* 3) Put path of the Saved Transformation Matrics(Nifti file) in Section 5.3 \"tmf_path\"\n", 282 | "* 4) put the desired folder path were you want to save the Segmented nifti with the original spacing \"path_to_save_images\" and RUN." 283 | ] 284 | }, 285 | { 286 | "cell_type": "markdown", 287 | "metadata": { 288 | "heading_collapsed": true, 289 | "hidden": true 290 | }, 291 | "source": [ 292 | "## Segmented Results" 293 | ] 294 | }, 295 | { 296 | "cell_type": "code", 297 | "execution_count": 35, 298 | "metadata": { 299 | "hidden": true 300 | }, 301 | "outputs": [ 302 | { 303 | "name": "stdout", 304 | "output_type": "stream", 305 | "text": [ 306 | "['IBSR_02_seg.nii.gz', 'IBSR_10_seg.nii.gz', 'IBSR_15_seg.nii.gz']\n" 307 | ] 308 | } 309 | ], 310 | "source": [ 311 | "Final_Seg_test_path=\"H:/f_r/final_test/\"\n", 312 | "My_Predicted_Seg_list=os.listdir(Final_Seg_test_path)\n", 313 | "print(My_Predicted_Seg_list)\n", 314 | "\n", 315 | "#complete_Segmented_data=Final_Seg_test_path+My_Predicted_Seg_list[0]\n", 316 | "#print(Segmented_data)" 317 | ] 318 | }, 319 | { 320 | "cell_type": "markdown", 321 | "metadata": { 322 | "heading_collapsed": true, 323 | "hidden": true 324 | }, 325 | "source": [ 326 | "## Raw Test Data " 327 | ] 328 | }, 329 | { 330 | "cell_type": "code", 331 | "execution_count": 36, 332 | "metadata": { 333 | "hidden": true 334 | }, 335 | "outputs": [ 336 | { 337 | "name": "stdout", 338 | "output_type": "stream", 339 | "text": [ 340 | "['IBSR_02.nii.gz', 'IBSR_10.nii.gz', 'IBSR_15.nii.gz']\n" 341 | ] 342 | } 343 | ], 344 | "source": [ 345 | "Test_data_raw_path=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Test_Data_Raw/\"\n", 346 | "Test_data_list=os.listdir(Test_data_raw_path)\n", 347 | "print(Test_data_list)\n", 348 | "\n", 349 | "#complete_RAW_data=Test_data_raw_path+Test_data_list[0]\n", 350 | "#print(complete_RAW_data)" 351 | ] 352 | }, 353 | { 354 | "cell_type": "markdown", 355 | "metadata": { 356 | "heading_collapsed": true, 357 | "hidden": true 358 | }, 359 | "source": [ 360 | "## Transformation Matrix" 361 | ] 362 | }, 363 | { 364 | "cell_type": "code", 365 | "execution_count": 37, 366 | "metadata": { 367 | "hidden": true 368 | }, 369 | "outputs": [ 370 | { 371 | "name": "stdout", 372 | "output_type": "stream", 373 | "text": [ 374 | "['IBSR_02.tfm', 'IBSR_10.tfm', 'IBSR_15.tfm']\n" 375 | ] 376 | } 377 | ], 378 | "source": [ 379 | "tmf_path=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/\"\n", 380 | "tmf_data_list=os.listdir(tmf_path)\n", 381 | "print(tmf_data_list)" 382 | ] 383 | }, 384 | { 385 | "cell_type": "markdown", 386 | "metadata": { 387 | "heading_collapsed": true, 388 | "hidden": true 389 | }, 390 | "source": [ 391 | "## Inverse Registration" 392 | ] 393 | }, 394 | { 395 | "cell_type": "code", 396 | "execution_count": 41, 397 | "metadata": { 398 | "hidden": true 399 | }, 400 | "outputs": [ 401 | { 402 | "name": "stdout", 403 | "output_type": "stream", 404 | "text": [ 405 | "H:/f_r/final_test/IBSR_02_seg.nii.gz\n", 406 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Test_Data_Raw/IBSR_02.nii.gz\n", 407 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_02.tfm\n", 408 | "IBSR_02\n", 409 | "H:/f_r/final_test/IBSR_10_seg.nii.gz\n", 410 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Test_Data_Raw/IBSR_10.nii.gz\n", 411 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_10.tfm\n", 412 | "IBSR_10\n", 413 | "H:/f_r/final_test/IBSR_15_seg.nii.gz\n", 414 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Test_Data_Raw/IBSR_15.nii.gz\n", 415 | "C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/Registered_Testdata_tmf_file/IBSR_15.tfm\n", 416 | "IBSR_15\n" 417 | ] 418 | } 419 | ], 420 | "source": [ 421 | "path_to_save_images=\"C:/Users/Fakrul-IslamTUSHAR/Documents/MISAProject/MisaPreProcessing/submission/\"\n", 422 | "\n", 423 | "for im in range(0,3):\n", 424 | " \n", 425 | " ##Creating Data Path\n", 426 | " complete_Segmented_data=Final_Seg_test_path+My_Predicted_Seg_list[im]\n", 427 | " complete_RAW_data=Test_data_raw_path+Test_data_list[im]\n", 428 | " complete_tmf_data=tmf_path+tmf_data_list[im]\n", 429 | " \n", 430 | " print(complete_Segmented_data)\n", 431 | " print(complete_RAW_data)\n", 432 | " print(complete_tmf_data)\n", 433 | " \n", 434 | " ###Getting Suvject MRI\n", 435 | " img_fn = str(complete_RAW_data)\n", 436 | " img_name = img_fn.split('/')[-1].split('.')[0]\n", 437 | " label_fn=str(complete_Segmented_data)\n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " print(img_name)\n", 442 | " \n", 443 | " ##Creating Name\n", 444 | " Registered_imageName=img_name+\"_seg\"+'.nii.gz'\n", 445 | " \n", 446 | " ##The Original Spaced Image\n", 447 | " Original_fixed=sitk.ReadImage(img_fn, sitk.sitkFloat32)\n", 448 | " \n", 449 | " ####Segmented Prediction\n", 450 | " label_Registered_moving=sitk.ReadImage(label_fn, sitk.sitkFloat32)\n", 451 | " \n", 452 | " ######Load the Transformation\n", 453 | " initial_transform_for_InterTransformation=sitk.ReadTransform(complete_tmf_data)\n", 454 | " inverse_Transformation=initial_transform_for_InterTransformation.GetInverse()\n", 455 | " \n", 456 | " Original_resampled_label = sitk.Resample(label_Registered_moving, Original_fixed, \n", 457 | " inverse_Transformation, sitk.sitkNearestNeighbor, 0.0, \n", 458 | " label_Registered_moving.GetPixelID())\n", 459 | " \n", 460 | " sitk.WriteImage(Original_resampled_label, os.path.join(path_to_save_images, Registered_imageName))\n", 461 | " \n", 462 | " \n", 463 | " " 464 | ] 465 | }, 466 | { 467 | "cell_type": "code", 468 | "execution_count": null, 469 | "metadata": { 470 | "hidden": true 471 | }, 472 | "outputs": [], 473 | "source": [] 474 | } 475 | ], 476 | "metadata": { 477 | "kernelspec": { 478 | "display_name": "Python 3", 479 | "language": "python", 480 | "name": "python3" 481 | }, 482 | "language_info": { 483 | "codemirror_mode": { 484 | "name": "ipython", 485 | "version": 3 486 | }, 487 | "file_extension": ".py", 488 | "mimetype": "text/x-python", 489 | "name": "python", 490 | "nbconvert_exporter": "python", 491 | "pygments_lexer": "ipython3", 492 | "version": "3.6.7" 493 | }, 494 | "toc": { 495 | "base_numbering": 1, 496 | "nav_menu": {}, 497 | "number_sections": true, 498 | "sideBar": true, 499 | "skip_h1_title": false, 500 | "title_cell": "Table of Contents", 501 | "title_sidebar": "Contents", 502 | "toc_cell": true, 503 | "toc_position": {}, 504 | "toc_section_display": true, 505 | "toc_window_display": true 506 | }, 507 | "varInspector": { 508 | "cols": { 509 | "lenName": 16, 510 | "lenType": 16, 511 | "lenVar": 40 512 | }, 513 | "kernels_config": { 514 | "python": { 515 | "delete_cmd_postfix": "", 516 | "delete_cmd_prefix": "del ", 517 | "library": "var_list.py", 518 | "varRefreshCmd": "print(var_dic_list())" 519 | }, 520 | "r": { 521 | "delete_cmd_postfix": ") ", 522 | "delete_cmd_prefix": "rm(", 523 | "library": "var_list.r", 524 | "varRefreshCmd": "cat(var_dic_list()) " 525 | } 526 | }, 527 | "types_to_exclude": [ 528 | "module", 529 | "function", 530 | "builtin_function_or_method", 531 | "instance", 532 | "_Feature" 533 | ], 534 | "window_display": false 535 | } 536 | }, 537 | "nbformat": 4, 538 | "nbformat_minor": 2 539 | } 540 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Brain Tissue Segmentation Using NeuroNet With Different Pre-processing Techniques 2 | This Repository is for the MISA Course final project which was Brain tissue segmentation. we adopt NeuroNet which is a comprehensive brain image segmentation tool based on a novel multi-output CNN architecture which has been trained and tuned using IBSR18 data. If you use this model in your work please refer to the Original NeuroNet Paper and DLTK at https://github.com/DLTK/models/tree/master/ukbb_neuronet_brain_segmentation 3 | 4 | If our work help in your task or project please site the work at https://ieeexplore.ieee.org/document/8858515 (Pre-print https://arxiv.org/abs/1904.00068 ). This work is been accepted for presented at 3rd International Conference on Imaging, Vision & Pattern Recognition (IVPR),2019. 5 | 6 | # Citation 7 | ``` 8 | F. I. Tushar, B. Alyafi, M. K. Hasan and L. Dahal, 9 | "Brain Tissue Segmentation Using NeuroNet With Different Pre-processing Techniques," 10 | 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) 11 | and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), 12 | Spokane, WA, USA, 2019, pp. 223-227. 13 | 14 | @INPROCEEDINGS{8858515, author={F. I. {Tushar} and B. {Alyafi} and M. K. {Hasan} and L. {Dahal}}, 15 | booktitle={2019 Joint 8th International Conference on Informatics, Electronics Vision (ICIEV) and 16 | 2019 3rd International Conference on Imaging, Vision Pattern Recognition (icIVPR)}, 17 | title={Brain Tissue Segmentation Using NeuroNet With Different Pre-processing Techniques}, 18 | year={2019},pages={223-227}, 19 | } 20 | ``` 21 | # Overview 22 | Automatic segmentation of MRI brain images is one of the vital steps for quantitative analysis of brain for further inspection. Since manual segmentation of brain tissues (white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF)) is a time-consuming and tedious task that engages valuable human resources, hence, automatic brain tissue segmentation draws an enormous amount of attention in medical imaging. In this project, NeuroNet has been adopted to segment the brain which uses Residual Network (ResNet) in encoder and Fully Convolution Network (FCN) in the decoder. To achieve the best performance, various hyper-parameters have been tuned, while, network parameters (kernel and bias) were initialized using the NeuroNet pre-trained model. Different pre-processing pipelines have also been introduced to get best a robust trained model. The performance of the segmented validation images were measured quantitatively using Dice Similarity Co-efficient (DSC) and were reported in the best case as 0.8986±0.0174 for CSF, 0.9412 ± 0.0086 for GM, and 0.9335 ± 0.0166 for WM. We worked out that keeping the original patch size and using histogram preprocessing with 4000 steps had the highest achievable performance. 23 | ![model Architecture](https://github.com/fitushar/Brain-Tissue-Segmentation-Using-Deep-Learning-Pipeline-NeuroNet/blob/master/Images/architecture.PNG) 24 | 25 | In this work two different pre-processing pipelines were implemented. To see the effect on the performance of the deep CNN with different pre-processing scheme. Figure below shown the overview of the pre-processing pipelines. 26 | 27 | ![Pre_Processing pipeline](https://github.com/fitushar/Brain-Tissue-Segmentation-Using-Deep-Learning-Pipeline-NeuroNet/blob/master/Images/Preprocessing_pipelines.PNG) 28 | 29 | ![Pre_Processing pipeline](https://github.com/fitushar/Brain-Tissue-Segmentation-Using-Deep-Learning-Pipeline-NeuroNet/blob/master/Images/example_preprpcessed.png) 30 | 31 | # How to Run the Code 32 | To run and model and re-produce the best results thins steps need to perform. 33 | 34 | 1. Run Notebook “MISA_Project_PreProcesing_Step(1)_Registration.ipynb” to perform the registration of the Volumes to MNI template. 35 | 2. Run Notebook “MISA_Project_PreProcesing_Step(2)_Normalization.ipynb” to Perform Preprocessing (Pre-processing pipeline-2 mentioned in report) and to create the excel files that containing the path of the training , validation and testing data. Network Read the data from excel files that have the path of the data. 36 | 3. Folder “Model” Contain the pretrained model, Download the Weights from here https://goo.gl/VmhGYc 37 | 4. To run the code please the command “python train.py --config config_spm_tissue.json” 38 | 5. In the file “config_spm_tissue.json” to maintain and configure model 39 | 40 | model_path": put ur model weights path (spm_tissue folder) 41 | 42 | 6. To prepare the Testing Data and After segmentation to bring it back to the original spacing use this Notebook “PreparingTestingData.ipynb” 43 | 7. To run the testing ““python deploy.py --config config_spm_tissue.json”” 44 | 8. Finaly to Compute the Dice and Box plot Run the “Evaluation_MISA_Project.ipynb” 45 | 46 | 47 | ![dsc](https://github.com/fitushar/Brain-Tissue-Segmentation-Using-Deep-Learning-Pipeline-NeuroNet/blob/master/Images/5_2.png) 48 | ![overlay](https://github.com/fitushar/Brain-Tissue-Segmentation-Using-Deep-Learning-Pipeline-NeuroNet/blob/master/Images/overlay_val14.png) 49 | 50 | 51 | -------------------------------------------------------------------------------- /model/README.md: -------------------------------------------------------------------------------- 1 | ## Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines 2 | 3 | ![Example prediction on test data](figures/example_seg.png) 4 | 5 | ### Contact and referencing this work 6 | If there are any issues please contact the corresponding author of this implementation. If you employ this model in your work, please refer to this citation of the [paper](https://openreview.net/pdf?id=Hks1TRisM). 7 | ``` 8 | @inproceedings{rajchl2018neuronet, 9 | title={NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines}, 10 | author={Martin Rajchl and Nick Pawlowski and Daniel Rueckert and Paul M. Matthews and Ben Glocker}, 11 | booktitle={International conference on Medical Imaging with Deep Learning (MIDL)}, 12 | year={2018} 13 | } 14 | ``` 15 | 16 | 17 | ### Data 18 | The data can be downloaded after registration from the [UK Biobank Imaging Enhancement Study website](https://imaging.ukbiobank.ac.uk/). 19 | 20 | Images and segmentations are read from a csv file in the format below. The original files (*.csv) is provided in this repo. 21 | 22 | These are parsed and extract tf.Tensor examples for training and evaluation in `reader.py` using a [SimpleITK](http://www.simpleitk.org/) for i/o of the .nii files. 23 | 24 | 25 | ### Usage 26 | Files: 27 | - `parse_csvs.ipynb` creates training/validation/testing .csv files from data paths and splits the subject ids into categories. 28 | - `sandbox.ipynb` visually assesses the outputs of the `reader.py` for a visual check of the inputs 29 | - `eval.ipynb` computes the visual and numerical results for the paper 30 | 31 | - `reader.py` dltk reader, containing the label mappings to and from consecutive ids and the python generator creating input tensors to the network, using a SimpleITK interface 32 | - `train.py` main training script to run all experiments with 33 | - `deploy.py` generic deploy script for all experiments 34 | 35 | - `config*.json` are configuration files to determine the dataset(s) to train on, scaling the flexible NeuroNet architecture and a few exposed training parameters. 36 | - `*.csv` csv files generated with `parse_csvs.ipynb`, containing the paths to all .nii image files 37 | 38 | 39 | #### Data Preprocessing 40 | We did not apply any data preprocessing, such as brain stripping or additional bias correction, etc. The input to the network is a single MNI registered 1mm isotropic T1-weighted MR image (as procude by the UK Biobank). Please refer to the [UKB Neuroimaging documentation](https://biobank.ctsu.ox.ac.uk/crystal/docs/brain_mri.pdf) for additional information. 41 | 42 | #### Training 43 | You can use the code (train.py) to train the model on the data yourself. Alternatively, we provide pretrained models from the paper here: 44 | - [neuronet_all](http://www.doc.ic.ac.uk/~mrajchl/dltk_models/model_zoo/neuronet/neuronet_all.tar.gz) 45 | - [neuronet_tissue](http://www.doc.ic.ac.uk/~mrajchl/dltk_models/model_zoo/neuronet/neuronet_tissue.tar.gz) 46 | - [neuronet_single fsl fast](http://www.doc.ic.ac.uk/~mrajchl/dltk_models/model_zoo/neuronet/fsl_fast.tar.gz) 47 | - [neuronet_single fsl first](http://www.doc.ic.ac.uk/~mrajchl/dltk_models/model_zoo/neuronet/fsl_first.tar.gz) 48 | - [neuronet_single spm tissue](http://www.doc.ic.ac.uk/~mrajchl/dltk_models/model_zoo/neuronet/spm_tissue.tar.gz) 49 | - [neuronet_single malp_em tissue](http://www.doc.ic.ac.uk/~mrajchl/dltk_models/model_zoo/neuronet/malp_em_tissue.tar.gz) 50 | - [neuronet_single malp_em](http://www.doc.ic.ac.uk/~mrajchl/dltk_models/model_zoo/neuronet/malp_em.tar.gz) 51 | 52 | 53 | Depending on the model, the number of output volumes will correspond with the number of segmentation tasks (i.e. neuronet_single will produce one volume, neuronet_all will produce 5 segmentation volumes). 54 | 55 | You can start a basic training with 56 | ``` 57 | python train.py -c CUDA_DEVICE --config MY_CONFIG 58 | ``` 59 | that will load the file paths from the previously created csvs, according to the config parameters. 60 | 61 | #### Deploy 62 | To deploy a model and run inference, run the deploy.py script and point to the model save_path: 63 | 64 | ``` 65 | python deploy.py -p path/to/saved/model -c CUDA_DEVICE --config MY_CONFIG 66 | ``` -------------------------------------------------------------------------------- /model/config_all.json: -------------------------------------------------------------------------------- 1 | { 2 | "protocols": ["fsl_fast", "fsl_first", "spm_tissue", "malp_em", "malp_em_tissue"], 3 | "num_classes": [4, 16, 4, 139, 6], 4 | "model_path": "/tmp/neuronet/models/neuronet_all", 5 | "out_segm_path": "/tmp/neuronet/out/neuronet_all", 6 | "learning_rate": 0.001, 7 | "network": { 8 | "filters": [16, 32, 64, 128], 9 | "strides": [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 10 | "num_residual_units": 2 11 | } 12 | } -------------------------------------------------------------------------------- /model/config_fsl_fast.json: -------------------------------------------------------------------------------- 1 | { 2 | "protocols": ["fsl_fast"], 3 | "num_classes": [4], 4 | "model_path": "/tmp/neuronet/models/fsl_fast", 5 | "out_segm_path": "/tmp/neuronet/out/fsl_fast", 6 | "learning_rate": 0.001, 7 | "network": { 8 | "filters": [16, 32, 64, 128], 9 | "strides": [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 10 | "num_residual_units": 2 11 | } 12 | } -------------------------------------------------------------------------------- /model/config_fsl_first.json: -------------------------------------------------------------------------------- 1 | { 2 | "protocols": ["fsl_first"], 3 | "num_classes": [16], 4 | "model_path": "/tmp/neuronet/models/fsl_first", 5 | "out_segm_path": "/tmp/neuronet/out/fsl_first", 6 | "learning_rate": 0.001, 7 | "network": { 8 | "filters": [16, 32, 64, 128], 9 | "strides": [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 10 | "num_residual_units": 2 11 | } 12 | } -------------------------------------------------------------------------------- /model/config_malp_em.json: -------------------------------------------------------------------------------- 1 | { 2 | "protocols": ["malp_em"], 3 | "num_classes": [139], 4 | "model_path": "/tmp/neuronet/models/malp_em", 5 | "out_segm_path": "/tmp/neuronet/out/malp_em", 6 | "learning_rate": 0.001, 7 | "network": { 8 | "filters": [16, 32, 64, 128], 9 | "strides": [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 10 | "num_residual_units": 2 11 | } 12 | } -------------------------------------------------------------------------------- /model/config_malp_em_tissue.json: -------------------------------------------------------------------------------- 1 | { 2 | "protocols": ["malp_em_tissue"], 3 | "num_classes": [6], 4 | "model_path": "/tmp/neuronet/models/malp_em_tissue", 5 | "out_segm_path": "/tmp/neuronet/out/malp_em_tissue", 6 | "learning_rate": 0.001, 7 | "network": { 8 | "filters": [16, 32, 64, 128], 9 | "strides": [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 10 | "num_residual_units": 2 11 | } 12 | } -------------------------------------------------------------------------------- /model/config_spm_tissue.json: -------------------------------------------------------------------------------- 1 | { 2 | "protocols": ["spm_tissue"], 3 | "num_classes": [4], 4 | "model_path": "/home/maia_kbf/MISA_FIT/nnet_fit/spm_tissue", 5 | "out_segm_path": "/tmp/neuronet/out/spm_tissue", 6 | "learning_rate": 0.001, 7 | "network": { 8 | "filters": [16, 32, 64, 128], 9 | "strides": [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 10 | "num_residual_units": 2 11 | } 12 | } -------------------------------------------------------------------------------- /model/config_tissue.json: -------------------------------------------------------------------------------- 1 | { 2 | "protocols": ["fsl_fast", "spm_tissue", "malp_em_tissue"], 3 | "num_classes": [4, 4, 6], 4 | "model_path": "/tmp/neuronet/models/neuronet_tissue", 5 | "out_segm_path": "/tmp/neuronet/out/neuronet_tissue", 6 | "learning_rate": 0.001, 7 | "network": { 8 | "filters": [16, 32, 64, 128], 9 | "strides": [[1, 1, 1], [2, 2, 2], [2, 2, 2], [2, 2, 2]], 10 | "num_residual_units": 2 11 | } 12 | } -------------------------------------------------------------------------------- /model/deploy.pvpy: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import division 3 | from __future__ import print_function 4 | 5 | import argparse 6 | import os 7 | import time 8 | import numpy as np 9 | import pandas as pd 10 | import tensorflow as tf 11 | from keras import backend as K 12 | ####Tensorflow wizard 13 | config=tf.ConfigProto() 14 | config.gpu_options.allow_growth=True 15 | config.gpu_options.per_process_gpu_memory_fraction=0.23 16 | K.tensorflow_backend.set_session(tf.Session(config=config)) 17 | 18 | 19 | 20 | import SimpleITK as sitk 21 | import json 22 | 23 | from tensorflow.contrib import predictor 24 | 25 | from dltk.utils import sliding_window_segmentation_inference 26 | 27 | from reader import read_fn, map_labels 28 | 29 | 30 | def predict(args, config): 31 | 32 | # Read in the csv with the file names you would want to predict on 33 | file_names = pd.read_csv(args.csv, 34 | dtype=object, 35 | keep_default_na=False, 36 | na_values=[]).as_matrix() 37 | 38 | # From the model model_path, parse the latest saved estimator model 39 | # and restore a predictor from it 40 | export_dir = [os.path.join(config["model_path"], o) for o in os.listdir(config["model_path"]) 41 | if os.path.isdir(os.path.join(config["model_path"], o)) and o.isdigit()][-1] 42 | print('Loading from {}'.format(export_dir)) 43 | my_predictor = predictor.from_saved_model(export_dir) 44 | 45 | protocols = config["protocols"] 46 | # Fetch the output probability ops of the trained network 47 | y_probs = [my_predictor._fetch_tensors['y_prob_{}'.format(p)] for p in protocols] 48 | 49 | # Iterate through the files, predict on the full volumes and 50 | # compute a Dice similariy coefficient 51 | for output in read_fn(file_references=file_names, 52 | mode=tf.estimator.ModeKeys.PREDICT, 53 | params={'extract_examples': False, 54 | 'protocols': protocols}): 55 | 56 | print('Running file {}'.format(output['img_id'])) 57 | t0 = time.time() 58 | 59 | # Parse the read function output and add a dummy batch dimension 60 | # as required 61 | img = np.expand_dims(output['features']['x'], axis=0) 62 | 63 | # Do a sliding window inference with our DLTK wrapper 64 | preds = sliding_window_segmentation_inference( 65 | session=my_predictor.session, 66 | ops_list=y_probs, 67 | sample_dict={my_predictor._feed_tensors['x']: img}, 68 | batch_size=2) 69 | 70 | # Calculate the prediction from the probabilities 71 | #preds = [np.squeeze(np.argmax(pred, -1), axis=0) for pred in preds] 72 | 73 | # Map the consecutive integer label ids back to the original ones 74 | #for i in range(len(protocols)): 75 | #preds[i] = map_labels(preds[i], 76 | #protocol=protocols[i], 77 | #convert_to_protocol=True) 78 | 79 | # Save the file as .nii.gz using the header information from the 80 | # original sitk image 81 | out_folder = os.path.join(config["out_segm_path"], '{}'.format(output['img_id'])) 82 | os.system('mkdir -p {}'.format(out_folder)) 83 | 84 | for i in range(len(protocols)): 85 | output_fn = os.path.join(out_folder, protocols[i] + '.nii.gz') 86 | new_sitk = sitk.GetImageFromArray(preds[i].astype(np.int32)) 87 | new_sitk.CopyInformation(output['sitk']) 88 | sitk.WriteImage(new_sitk,"{}_Seg.nii.gz".format(output['img_id'])) 89 | 90 | 91 | 92 | # Print outputs 93 | print('ID={}; input_dim={}; time={};'.format( 94 | output['img_id'], img.shape, time.time() - t0)) 95 | 96 | 97 | if __name__ == '__main__': 98 | # Set up argument parser 99 | parser = argparse.ArgumentParser(description='Neuronet deploy script') 100 | parser.add_argument('--verbose', default=False, action='store_true') 101 | parser.add_argument('--cuda_devices', '-c', default='0') 102 | 103 | parser.add_argument('--csv', default='val.csv') 104 | parser.add_argument('--config', default='config_all.json') 105 | 106 | args = parser.parse_args() 107 | 108 | # Set verbosity 109 | if args.verbose: 110 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' 111 | tf.logging.set_verbosity(tf.logging.INFO) 112 | else: 113 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 114 | tf.logging.set_verbosity(tf.logging.ERROR) 115 | 116 | # GPU allocation options 117 | os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda_devices 118 | 119 | # Parse the run config 120 | with open(args.config) as f: 121 | config = json.load(f) 122 | 123 | # Call training 124 | predict(args, config) 125 | -------------------------------------------------------------------------------- /model/deploy.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import division 3 | from __future__ import print_function 4 | 5 | import argparse 6 | import os 7 | import time 8 | import numpy as np 9 | import pandas as pd 10 | import tensorflow as tf 11 | from keras import backend as K 12 | ####Tensorflow wizard 13 | config=tf.ConfigProto() 14 | config.gpu_options.allow_growth=True 15 | config.gpu_options.per_process_gpu_memory_fraction=0.23 16 | K.tensorflow_backend.set_session(tf.Session(config=config)) 17 | 18 | 19 | 20 | import SimpleITK as sitk 21 | import json 22 | 23 | from tensorflow.contrib import predictor 24 | 25 | from dltk.utils import sliding_window_segmentation_inference 26 | 27 | from reader import read_fn, map_labels 28 | 29 | 30 | def predict(args, config): 31 | 32 | # Read in the csv with the file names you would want to predict on 33 | file_names = pd.read_csv(args.csv, 34 | dtype=object, 35 | keep_default_na=False, 36 | na_values=[]).as_matrix() 37 | 38 | # From the model model_path, parse the latest saved estimator model 39 | # and restore a predictor from it 40 | export_dir = [os.path.join(config["model_path"], o) for o in os.listdir(config["model_path"]) 41 | if os.path.isdir(os.path.join(config["model_path"], o)) and o.isdigit()][-1] 42 | print('Loading from {}'.format(export_dir)) 43 | my_predictor = predictor.from_saved_model(export_dir) 44 | 45 | protocols = config["protocols"] 46 | # Fetch the output probability ops of the trained network 47 | y_probs = [my_predictor._fetch_tensors['y_prob_{}'.format(p)] for p in protocols] 48 | 49 | # Iterate through the files, predict on the full volumes and 50 | # compute a Dice similariy coefficient 51 | for output in read_fn(file_references=file_names, 52 | mode=tf.estimator.ModeKeys.PREDICT, 53 | params={'extract_examples': False, 54 | 'protocols': protocols}): 55 | 56 | print('Running file {}'.format(output['img_id'])) 57 | t0 = time.time() 58 | 59 | # Parse the read function output and add a dummy batch dimension 60 | # as required 61 | img = np.expand_dims(output['features']['x'], axis=0) 62 | 63 | # Do a sliding window inference with our DLTK wrapper 64 | preds = sliding_window_segmentation_inference( 65 | session=my_predictor.session, 66 | ops_list=y_probs, 67 | sample_dict={my_predictor._feed_tensors['x']: img}, 68 | batch_size=2) 69 | 70 | # Calculate the prediction from the probabilities 71 | preds = [np.squeeze(np.argmax(pred, -1), axis=0) for pred in preds] 72 | 73 | # Map the consecutive integer label ids back to the original ones 74 | for i in range(len(protocols)): 75 | preds[i] = map_labels(preds[i], 76 | protocol=protocols[i], 77 | convert_to_protocol=True) 78 | 79 | # Save the file as .nii.gz using the header information from the 80 | # original sitk image 81 | out_folder = os.path.join(config["out_segm_path"], '{}'.format(output['img_id'])) 82 | os.system('mkdir -p {}'.format(out_folder)) 83 | 84 | for i in range(len(protocols)): 85 | output_fn = os.path.join(out_folder, protocols[i] + '.nii.gz') 86 | new_sitk = sitk.GetImageFromArray(preds[i].astype(np.int32)) 87 | new_sitk.CopyInformation(output['sitk']) 88 | sitk.WriteImage(new_sitk,"{}_Seg.nii.gz".format(output['img_id'])) 89 | 90 | 91 | 92 | # Print outputs 93 | print('ID={}; input_dim={}; time={};'.format( 94 | output['img_id'], img.shape, time.time() - t0)) 95 | 96 | 97 | if __name__ == '__main__': 98 | # Set up argument parser 99 | parser = argparse.ArgumentParser(description='Neuronet deploy script') 100 | parser.add_argument('--verbose', default=False, action='store_true') 101 | parser.add_argument('--cuda_devices', '-c', default='0') 102 | 103 | parser.add_argument('--csv', default='test.csv') 104 | parser.add_argument('--config', default='config_all.json') 105 | 106 | args = parser.parse_args() 107 | 108 | # Set verbosity 109 | if args.verbose: 110 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' 111 | tf.logging.set_verbosity(tf.logging.INFO) 112 | else: 113 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 114 | tf.logging.set_verbosity(tf.logging.ERROR) 115 | 116 | # GPU allocation options 117 | os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda_devices 118 | 119 | # Parse the run config 120 | with open(args.config) as f: 121 | config = json.load(f) 122 | 123 | # Call training 124 | predict(args, config) 125 | -------------------------------------------------------------------------------- /model/dim.nii: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fitushar/Brain-Tissue-Segmentation-Using-Deep-Learning-Pipeline-NeuroNet/e3d33136d34a9ea4b55b3e210c78271980b683e2/model/dim.nii -------------------------------------------------------------------------------- /model/neuronet.py: -------------------------------------------------------------------------------- 1 | from __future__ import unicode_literals 2 | from __future__ import print_function 3 | from __future__ import division 4 | from __future__ import absolute_import 5 | 6 | import tensorflow as tf 7 | 8 | from dltk.core.residual_unit import vanilla_residual_unit_3d 9 | from dltk.core.upsample import linear_upsample_3d 10 | 11 | 12 | def upscore_layer_3d(inputs, 13 | inputs2, 14 | out_filters, 15 | in_filters=None, 16 | strides=(2, 2, 2), 17 | mode=tf.estimator.ModeKeys.EVAL, use_bias=False, 18 | kernel_initializer=tf.initializers.variance_scaling(distribution='uniform'), 19 | bias_initializer=tf.zeros_initializer(), 20 | kernel_regularizer=None, 21 | bias_regularizer=None): 22 | """Upscore layer according to [1]. 23 | 24 | [1] J. Long et al. Fully convolutional networks for semantic segmentation. 25 | CVPR 2015. 26 | 27 | Args: 28 | inputs (tf.Tensor): Input features to be upscored. 29 | inputs2 (tf.Tensor): Higher resolution features from the encoder to add. 30 | out_filters (int): Number of output filters (typically, number of 31 | segmentation classes) 32 | in_filters (None, optional): None or number of input filters. 33 | strides (tuple, optional): Upsampling factor for a strided transpose 34 | convolution. 35 | mode (TYPE, optional): One of the tf.estimator.ModeKeys strings: TRAIN, 36 | EVAL or PREDICT 37 | use_bias (bool, optional): Boolean, whether the layer uses a bias. 38 | kernel_initializer (TYPE, optional): An initializer for the convolution 39 | kernel. 40 | bias_initializer (TYPE, optional): An initializer for the bias vector. 41 | If None, no bias will be applied. 42 | kernel_regularizer (None, optional): Optional regularizer for the 43 | convolution kernel. 44 | bias_regularizer (None, optional): Optional regularizer for the bias 45 | vector. 46 | 47 | Returns: 48 | tf.Tensor: Upscore tensor 49 | 50 | """ 51 | conv_params = {'use_bias': use_bias, 52 | 'kernel_initializer': kernel_initializer, 53 | 'bias_initializer': bias_initializer, 54 | 'kernel_regularizer': kernel_regularizer, 55 | 'bias_regularizer': bias_regularizer} 56 | 57 | # Compute an upsampling shape dynamically from the input tensor. Input 58 | # filters are required to be static. 59 | if in_filters is None: 60 | in_filters = inputs.get_shape().as_list()[-1] 61 | 62 | assert len(inputs.get_shape().as_list()) == 5, \ 63 | 'inputs are required to have a rank of 5.' 64 | assert len(inputs.get_shape().as_list()) == len(inputs2.get_shape().as_list()), \ 65 | 'Ranks of input and input2 differ' 66 | 67 | # Account for differences in the number of input and output filters 68 | if in_filters != out_filters: 69 | x = tf.layers.conv3d(inputs=inputs, 70 | filters=out_filters, 71 | kernel_size=(1, 1, 1), 72 | strides=(1, 1, 1), 73 | padding='same', 74 | name='filter_conversion', 75 | **conv_params) 76 | else: 77 | x = inputs 78 | 79 | # Upsample inputs 80 | x = linear_upsample_3d(inputs=x, strides=strides) 81 | 82 | # Skip connection 83 | x2 = tf.layers.conv3d(inputs=inputs2, 84 | filters=out_filters, 85 | kernel_size=(1, 1, 1), 86 | strides=(1, 1, 1), 87 | padding='same', 88 | **conv_params) 89 | 90 | x2 = tf.layers.batch_normalization( 91 | x2, training=mode == tf.estimator.ModeKeys.TRAIN) 92 | 93 | # Return the element-wise sum 94 | return tf.add(x, x2) 95 | 96 | 97 | def neuronet_3d(inputs, 98 | num_classes, 99 | protocols, 100 | num_res_units=2, 101 | filters=(16, 32, 64, 128), 102 | strides=((1, 1, 1), (2, 2, 2), (2, 2, 2), (2, 2, 2)), 103 | mode=tf.estimator.ModeKeys.EVAL, 104 | use_bias=False, 105 | activation=tf.nn.relu6, 106 | kernel_initializer=tf.initializers.variance_scaling(distribution='uniform'), 107 | bias_initializer=tf.zeros_initializer(), 108 | kernel_regularizer=None, 109 | bias_regularizer=None): 110 | """ 111 | NeuroNet [1] is a multi-task image segmentation network based on an FCN 112 | architecture [2] using residual units [3] as feature extractors. 113 | Downsampling and upsampling of features is done via strided convolutions 114 | and transpose convolutions, respectively. On each resolution scale s 115 | are num_residual_units with filter size = filters[s]. strides[s] determine 116 | the downsampling factor at each resolution scale. 117 | 118 | [1] M. Rajchl et al. NeuroNet: Fast and Robust Reproduction of Multiple 119 | Brain Image Segmentation Pipelines. MIDL 2018. 120 | 121 | [2] J. Long et al. Fully convolutional networks for semantic segmentation. 122 | CVPR 2015. 123 | [3] K. He et al. Identity Mappings in Deep Residual Networks. ECCV 2016. 124 | 125 | Args: 126 | inputs (tf.Tensor): Input feature tensor to the network (rank 5 127 | required). 128 | num_classes (int): Number of output classes. 129 | num_res_units (int, optional): Number of residual units at each 130 | resolution scale. 131 | filters (tuple, optional): Number of filters for all residual units at 132 | each resolution scale. 133 | strides (tuple, optional): Stride of the first unit on a resolution 134 | scale. 135 | mode (TYPE, optional): One of the tf.estimator.ModeKeys strings: 136 | TRAIN, EVAL or PREDICT 137 | use_bias (bool, optional): Boolean, whether the layer uses a bias. 138 | kernel_initializer (TYPE, optional): An initializer for the convolution 139 | kernel. 140 | bias_initializer (TYPE, optional): An initializer for the bias vector. 141 | If None, no bias will be applied. 142 | kernel_regularizer (None, optional): Optional regularizer for the 143 | convolution kernel. 144 | bias_regularizer (None, optional): Optional regularizer for the bias 145 | vector. 146 | 147 | Returns: 148 | dict: dictionary of output tensors 149 | """ 150 | outputs = {} 151 | assert len(strides) == len(filters) 152 | assert len(inputs.get_shape().as_list()) == 5, \ 153 | 'inputs are required to have a rank of 5.' 154 | assert len(protocols) == len(num_classes) 155 | 156 | conv_params = {'use_bias': use_bias, 157 | 'kernel_initializer': kernel_initializer, 158 | 'bias_initializer': bias_initializer, 159 | 'kernel_regularizer': kernel_regularizer, 160 | 'bias_regularizer': bias_regularizer} 161 | 162 | x = inputs 163 | 164 | # Inital convolution with filters[0] 165 | x = tf.layers.conv3d(inputs=x, 166 | filters=filters[0], 167 | kernel_size=(3, 3, 3), 168 | strides=strides[0], 169 | padding='same', 170 | **conv_params) 171 | 172 | tf.logging.info('Init conv tensor shape {}'.format(x.get_shape())) 173 | 174 | # Residual feature encoding blocks with num_res_units at different 175 | # resolution scales res_scales 176 | res_scales = [x] 177 | saved_strides = [] 178 | with tf.variable_scope('encoder'): 179 | for res_scale in range(1, len(filters)): 180 | 181 | # Features are downsampled via strided convolutions. These are defined 182 | # in `strides` and subsequently saved 183 | with tf.variable_scope('unit_{}_0'.format(res_scale)): 184 | 185 | x = vanilla_residual_unit_3d( 186 | inputs=x, 187 | out_filters=filters[res_scale], 188 | strides=strides[res_scale], 189 | mode=mode) 190 | saved_strides.append(strides[res_scale]) 191 | 192 | for i in range(1, num_res_units): 193 | 194 | with tf.variable_scope('unit_{}_{}'.format(res_scale, i)): 195 | 196 | x = vanilla_residual_unit_3d( 197 | inputs=x, 198 | out_filters=filters[res_scale], 199 | strides=(1, 1, 1), 200 | mode=mode) 201 | res_scales.append(x) 202 | 203 | tf.logging.info('Encoder at res_scale {} tensor shape: {}'.format( 204 | res_scale, x.get_shape())) 205 | 206 | outputs['encoder_out'] = x 207 | 208 | tails = [] 209 | for tail in range(len(num_classes)): 210 | # Create a separate prediction tail for each labeling protocol to learn 211 | with tf.variable_scope('tail_{}'.format(tail)): 212 | x = outputs['encoder_out'] 213 | 214 | for res_scale in range(len(filters) - 2, -1, -1): 215 | # Upscore layers [2] reconstruct the predictions to 216 | # higher resolution scales 217 | with tf.variable_scope('upscore_{}'.format(res_scale)): 218 | x = upscore_layer_3d( 219 | inputs=x, 220 | inputs2=res_scales[res_scale], 221 | out_filters=num_classes[tail], 222 | strides=saved_strides[res_scale], 223 | mode=mode, 224 | **conv_params) 225 | 226 | tf.logging.info('Decoder at res_scale {} tensor shape: {}'.format( 227 | res_scale, x.get_shape())) 228 | 229 | # Last convolution 230 | with tf.variable_scope('last'): 231 | tails.append(tf.layers.conv3d(inputs=x, 232 | filters=num_classes[tail], 233 | kernel_size=(1, 1, 1), 234 | strides=(1, 1, 1), 235 | padding='same', 236 | **conv_params)) 237 | 238 | tf.logging.info('Output tensor shape {}'.format(x.get_shape())) 239 | 240 | # Define the outputs 241 | for i in range(len(tails)): 242 | outputs['logits_{}'.format(protocols[i])] = tails[i] 243 | 244 | with tf.variable_scope('pred'): 245 | outputs['y_prob_{}'.format(protocols[i])] = tf.nn.softmax(tails[i]) 246 | outputs['y_{}'.format(protocols[i])] = tf.argmax(tails[i], axis=-1) 247 | 248 | return outputs 249 | -------------------------------------------------------------------------------- /model/neuronet.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fitushar/Brain-Tissue-Segmentation-Using-Deep-Learning-Pipeline-NeuroNet/e3d33136d34a9ea4b55b3e210c78271980b683e2/model/neuronet.pyc -------------------------------------------------------------------------------- /model/parse_csvs.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 10, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import os\n", 10 | "import pandas as pd\n", 11 | "import numpy as np" 12 | ] 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 8, 17 | "metadata": {}, 18 | "outputs": [ 19 | { 20 | "name": "stdout", 21 | "output_type": "stream", 22 | "text": [ 23 | "['fsl_fast']\n" 24 | ] 25 | } 26 | ], 27 | "source": [ 28 | "import json\n", 29 | "config_fn = 'fsl_fast_config.json'\n", 30 | "with open(config_fn) as f:\n", 31 | " config = json.load(f)\n", 32 | "print (config['targets']['protocols'])" 33 | ] 34 | }, 35 | { 36 | "cell_type": "code", 37 | "execution_count": 35, 38 | "metadata": { 39 | "collapsed": true 40 | }, 41 | "outputs": [], 42 | "source": [ 43 | "# paths\n", 44 | "base_path = '/vol/biobank/12579/brain/images'\n", 45 | "\n", 46 | "# list all ids \n", 47 | "all_ids = sorted(os.listdir(base_path))\n", 48 | "\n", 49 | "# check if all of them contain a T1w image and required segmentation(s)\n", 50 | "valid_ids = []\n", 51 | "for i in all_ids:\n", 52 | " if (os.path.isfile(os.path.join(base_path, i,'T1.nii.gz')) and \n", 53 | " os.path.isfile(os.path.join(base_path, i,'T1_first_all_fast_firstseg.nii.gz')) and\n", 54 | " os.path.isfile(os.path.join(base_path, i,'T1_brain_seg.nii.gz')) and\n", 55 | " os.path.isfile(os.path.join(base_path, i,'T1_brain_seg_spm.nii.gz')) and\n", 56 | " os.path.isfile(os.path.join(base_path, i,'T1_MALPEM.nii.gz')) and\n", 57 | " os.path.isfile(os.path.join(base_path, i,'T1_MALPEM_tissues.nii.gz'))):\n", 58 | " valid_ids.append(i)\n" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": 36, 64 | "metadata": {}, 65 | "outputs": [ 66 | { 67 | "name": "stdout", 68 | "output_type": "stream", 69 | "text": [ 70 | "5723\n" 71 | ] 72 | } 73 | ], 74 | "source": [ 75 | "print(len(valid_ids))" 76 | ] 77 | }, 78 | { 79 | "cell_type": "code", 80 | "execution_count": 37, 81 | "metadata": { 82 | "collapsed": true 83 | }, 84 | "outputs": [], 85 | "source": [ 86 | "def get_full_paths(_id, fn):\n", 87 | " return os.path.join(base_path, _id, fn)\n", 88 | "\n", 89 | "hdr = ['id', 't1', 'fsl_fast', 'fsl_first', 'spm_tissue', 'malp_em', 'malp_em_tissue']\n", 90 | "valid_df = []\n", 91 | "for i in valid_ids:\n", 92 | " valid_df.append([i, \n", 93 | " get_full_paths(i, 'T1.nii.gz'),\n", 94 | " get_full_paths(i, 'T1_brain_seg.nii.gz'),\n", 95 | " get_full_paths(i, 'T1_first_all_fast_firstseg.nii.gz'),\n", 96 | " get_full_paths(i, 'T1_brain_seg_spm.nii.gz'),\n", 97 | " get_full_paths(i, 'T1_MALPEM.nii.gz'),\n", 98 | " get_full_paths(i, 'T1_MALPEM_tissues.nii.gz')])\n", 99 | "\n" 100 | ] 101 | }, 102 | { 103 | "cell_type": "code", 104 | "execution_count": 38, 105 | "metadata": {}, 106 | "outputs": [ 107 | { 108 | "name": "stdout", 109 | "output_type": "stream", 110 | "text": [ 111 | "['1000845', '/vol/biobank/12579/brain/images/1000845/T1.nii.gz', '/vol/biobank/12579/brain/images/1000845/T1_brain_seg.nii.gz', '/vol/biobank/12579/brain/images/1000845/T1_first_all_fast_firstseg.nii.gz', '/vol/biobank/12579/brain/images/1000845/T1_brain_seg_spm.nii.gz', '/vol/biobank/12579/brain/images/1000845/T1_MALPEM.nii.gz', '/vol/biobank/12579/brain/images/1000845/T1_MALPEM_tissues.nii.gz']\n", 112 | "(5723, 7)\n" 113 | ] 114 | } 115 | ], 116 | "source": [ 117 | "print(valid_df[0])\n", 118 | "print(np.array(valid_df).shape)\n" 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": 39, 124 | "metadata": {}, 125 | "outputs": [ 126 | { 127 | "name": "stdout", 128 | "output_type": "stream", 129 | "text": [ 130 | "5000\n", 131 | "10\n", 132 | "713\n" 133 | ] 134 | } 135 | ], 136 | "source": [ 137 | "\n", 138 | "# 5k training ids\n", 139 | "write_df = valid_df[:5000]\n", 140 | "pd.DataFrame(write_df).to_csv('train.csv', index=False, header=hdr)\n", 141 | "print(len(write_df))\n", 142 | "\n", 143 | "# 10 validation ids\n", 144 | "write_df = valid_df[5000:5010]\n", 145 | "pd.DataFrame(write_df).to_csv('val.csv', index=False, header=hdr)\n", 146 | "print(len(write_df))\n", 147 | "\n", 148 | "# 713 test ids\n", 149 | "write_df = valid_df[5010:]\n", 150 | "pd.DataFrame(write_df).to_csv('test.csv', index=False, header=hdr)\n", 151 | "print(len(write_df))" 152 | ] 153 | }, 154 | { 155 | "cell_type": "code", 156 | "execution_count": null, 157 | "metadata": {}, 158 | "outputs": [], 159 | "source": [] 160 | } 161 | ], 162 | "metadata": { 163 | "kernelspec": { 164 | "display_name": "Python 3", 165 | "language": "python", 166 | "name": "python3" 167 | }, 168 | "language_info": { 169 | "codemirror_mode": { 170 | "name": "ipython", 171 | "version": 3 172 | }, 173 | "file_extension": ".py", 174 | "mimetype": "text/x-python", 175 | "name": "python", 176 | "nbconvert_exporter": "python", 177 | "pygments_lexer": "ipython3", 178 | "version": "3.5.2" 179 | } 180 | }, 181 | "nbformat": 4, 182 | "nbformat_minor": 2 183 | } 184 | -------------------------------------------------------------------------------- /model/reader.py: -------------------------------------------------------------------------------- 1 | import SimpleITK as sitk 2 | import tensorflow as tf 3 | import numpy as np 4 | 5 | from dltk.io.augmentation import extract_random_example_array 6 | from dltk.io.preprocessing import whitening 7 | 8 | ALL_PROTOCOLS = ['spm_tissue', 'fsl_first', 'fast_tissue', 'malp_em', 'malp_em_tissue'] 9 | NUM_CLASSES = [4, 16, 4, 139, 6] 10 | 11 | 12 | def map_labels(lbl, protocol=None, convert_to_protocol=False): 13 | """ 14 | Map dataset specific label id protocols to consecutive integer ids for training and back. 15 | Parameters 16 | ---------- 17 | lbl : np.array 18 | a label map 19 | protocol : str 20 | a string describing the labeling protocol 21 | convert_to_protocol : bool 22 | flag to determine to convert from or to the protocol ids 23 | """ 24 | 25 | """ 26 | SPM tissue ids: 27 | 0 Background 28 | 1 CSF 29 | 2 GM 30 | 3 WM 31 | """ 32 | spm_tissue_ids = range(4) 33 | 34 | """ 35 | Fast ids: 36 | 0 Background 37 | 1 CSF 38 | 2 GM 39 | 3 WM 40 | """ 41 | fast_ids = range(4) 42 | 43 | """ 44 | First ids: 45 | 0 Background 46 | 10 Left-Thalamus-Proper 40 47 | 11 Left-Caudate 30 48 | 12 Left-Putamen 40 49 | 13 Left-Pallidum 40 50 | 16 Brain-Stem /4th Ventricle 40 51 | 17 Left-Hippocampus 30 52 | 18 Left-Amygdala 50 53 | 26 Left-Accumbens-area 50 54 | 49 Right-Thalamus-Proper 40 55 | 50 Right-Caudate 30 56 | 51 Right-Putamen 40 57 | 52 Right-Pallidum 40 58 | 53 Right-Hippocampus 30 59 | 54 Right-Amygdala 50 60 | 58 Right-Accumbens-area 50 61 | """ 62 | first_ids = [0, 10, 11, 12, 13, 16, 17, 18, 26, 49, 50, 51, 52, 53, 54, 58] 63 | 64 | """ 65 | MALP-EM tissue ids: 66 | 0 Background 67 | 1 Ventricles 68 | 2 Sub-cortical and cerebellum GM 69 | 3 WM 70 | 4 Cortical GM 71 | 5 72 | """ 73 | malpem_tissue_ids = range(6) 74 | 75 | """ 76 | MALP-EM ids: 77 | 0 Background 78 | 1 3rdVentricle 79 | 2 4thVentricle 80 | 3 RightAccumbensArea 81 | 4 LeftAccumbensArea 82 | 5 RightAmygdala 83 | 6 LeftAmygdala 84 | 7 BrainStem 85 | 8 RightCaudate 86 | 9 LeftCaudate 87 | 10 RightCerebellumExterior 88 | 11 LeftCerebellumExterior 89 | 12 RightCerebellumWhiteMatter 90 | 13 LeftCerebellumWhiteMatter 91 | 14 RightCerebralExterior 92 | 15 LeftCerebralExterior 93 | 16 RightCerebralWhiteMatter 94 | 17 LeftCerebralWhiteMatter 95 | 18 CSF 96 | 19 RightHippocampus 97 | 20 LeftHippocampus 98 | 21 RightInfLatVent 99 | 22 LeftInfLatVent 100 | 23 RightLateralVentricle 101 | 24 LeftLateralVentricle 102 | 25 RightPallidum 103 | 26 LeftPallidum 104 | 27 RightPutamen 105 | 28 LeftPutamen 106 | 29 RightThalamusProper 107 | 30 LeftThalamusProper 108 | 31 RightVentralDC 109 | 32 LeftVentralDC 110 | 33 Rightvessel 111 | 34 Leftvessel 112 | 35 OpticChiasm 113 | 36 CerebellarVermalLobulesI-V 114 | 37 CerebellarVermalLobulesVI-VII 115 | 38 CerebellarVermalLobulesVIII-X 116 | 39 LeftBasalForebrain 117 | 40 RightBasalForebrain 118 | 41 RightACg Ganteriorcingulategyrus Right 119 | 42 LeftACg Ganteriorcingulategyrus Left 120 | 43 RightAIns Anteriorinsula Right 121 | 44 LeftAIns Anteriorinsula Left 122 | 45 RightAOrG Anteriororbitalgyrus Right 123 | 46 LeftAOrG Anteriororbitalgyrus Left 124 | 47 RightAnG Angulargyrus Right 125 | 48 LeftAnG Angulargyrus Left 126 | 49 RightCalc Calcarinecortex Right 127 | 50 LeftCalc Calcarinecortex Left 128 | 51 RightCO Centraloperculum Right 129 | 52 LeftCO Centraloperculum Left 130 | 53 RightCun Cuneus Right 131 | 54 LeftCun Cuneus Left 132 | 55 RightEntA Ententorhinalarea Right 133 | 56 LeftEntA Ententorhinalarea Left 134 | 57 RightFO Frontaloperculum Right 135 | 58 LeftFO Frontaloperculum Left 136 | 59 RightFRP Frontalpole Right 137 | 60 LeftFRP Frontalpole Left 138 | 61 RightFuG Fusiformgyrus Right 139 | 62 LeftFuG Fusiformgyrus Left 140 | 63 RightGRe Gyrusrectus Right 141 | 64 LeftGRe Gyrusrectus Left 142 | 65 RightIOG Inferioroccipitalgyrus Right 143 | 66 LeftIOG Inferioroccipitalgyrus Left 144 | 67 RightITG Inferiortemporalgyrus Right 145 | 68 LeftITG Inferiortemporalgyrus Left 146 | 69 RightLiG Lingualgyrus Right 147 | 70 LeftLiG Lingualgyrus Left 148 | 71 RightLOrG Lateralorbitalgyrus Right 149 | 72 LeftLOrG Lateralorbitalgyrus Left 150 | 73 RightMCgG Middlecingulategyrus Right 151 | 74 LeftMCgG Middlecingulategyrus Left 152 | 75 RightMFC Medialfrontalcortex Right 153 | 76 LeftMFC Medialfrontalcortex Left 154 | 77 RightMFG Middlefrontalgyrus Right 155 | 78 LeftMFG Middlefrontalgyrus Left 156 | 79 RightMOG Middleoccipitalgyrus Right 157 | 80 LeftMOG Middleoccipitalgyrus Left 158 | 81 RightMOrG Medialorbitalgyrus Right 159 | 82 LeftMOrG Medialorbitalgyrus Left 160 | 83 RightMPoG Postcentralgyrusmedialsegment Right 161 | 84 LeftMPoG Postcentralgyrusmedialsegment Left 162 | 85 RightMPrG Precentralgyrusmedialsegment Right 163 | 86 LeftMPrG Precentralgyrusmedialsegment Left 164 | 87 RightMSFG Superiorfrontalgyrusmedialsegment Right 165 | 88 LeftMSFG Superiorfrontalgyrusmedialsegment Left 166 | 89 RightMTG Middletemporalgyrus Right 167 | 90 LeftMTG Middletemporalgyrus Left 168 | 91 RightOCP Occipitalpole Right 169 | 92 LeftOCP Occipitalpole Left 170 | 93 RightOFuG Occipitalfusiformgyrus Right 171 | 94 LeftOFuG Occipitalfusiformgyrus Left 172 | 95 RightOpIFG Opercularpartoftheinferiorfrontalgyrus Right 173 | 96 LeftOpIFG Opercularpartoftheinferiorfrontalgyrus Left 174 | 97 RightOrIFG Orbitalpartoftheinferiorfrontalgyrus Right 175 | 98 LeftOrIFG Orbitalpartoftheinferiorfrontalgyrus Left 176 | 99 RightPCgG Posteriorcingulategyrus Right 177 | 100 LeftPCgG Posteriorcingulategyrus Left 178 | 101 RightPCu Precuneus Right 179 | 102 LeftPCu Precuneus Left 180 | 103 RightPHG Parahippocampalgyrus Right 181 | 104 LeftPHG Parahippocampalgyrus Left 182 | 105 RightPIns Posteriorinsula Right 183 | 106 LeftPIns Posteriorinsula Left 184 | 107 RightPO Parietaloperculum Right 185 | 108 LeftPO Parietaloperculum Left 186 | 109 RightPoG Postcentralgyrus Right 187 | 110 LeftPoG Postcentralgyrus Left 188 | 111 RightPOrG Posteriororbitalgyrus Right 189 | 112 LeftPOrG Posteriororbitalgyrus Left 190 | 113 RightPP Planumpolare Right 191 | 114 LeftPP Planumpolare Left 192 | 115 RightPrG Precentralgyrus Right 193 | 116 LeftPrG Precentralgyrus Left 194 | 117 RightPT Planumtemporale Right 195 | 118 LeftPT Planumtemporale Left 196 | 119 RightSCA Subcallosalarea Right 197 | 120 LeftSCA Subcallosalarea Left 198 | 121 RightSFG Superiorfrontalgyrus Right 199 | 122 LeftSFG Superiorfrontalgyrus Left 200 | 123 RightSMC Supplementarymotorcortex Right 201 | 124 LeftSMC Supplementarymotorcortex Left 202 | 125 RightSMG Supramarginalgyrus Right 203 | 126 LeftSMG Supramarginalgyrus Left 204 | 127 RightSOG Superioroccipitalgyrus Right 205 | 128 LeftSOG Superioroccipitalgyrus Left 206 | 129 RightSPL Superiorparietallobule Right 207 | 130 LeftSPL Superiorparietallobule Left 208 | 131 RightSTG Superiortemporalgyrus Right 209 | 132 LeftSTG Superiortemporalgyrus Left 210 | 133 RightTMP Temporalpole Right 211 | 134 LeftTMP Temporalpole Left 212 | 135 RightTrIFG Triangularpartoftheinferiorfrontalgyrus Right 213 | 136 LeftTrIFG Triangularpartoftheinferiorfrontalgyrus Left 214 | 137 RightTTG Transversetemporalgyrus Right 215 | 138 LeftTTG Transversetemporalgyrus Left 216 | """ 217 | malpem_ids = range(139) 218 | 219 | out_lbl = np.zeros_like(lbl) 220 | 221 | if protocol == 'fsl_fast': 222 | ids = fast_ids 223 | elif protocol == 'fsl_first': 224 | ids = first_ids 225 | elif protocol == 'spm_tissue': 226 | ids = spm_tissue_ids 227 | elif protocol == 'malp_em': 228 | ids = malpem_ids 229 | elif protocol == 'malp_em_tissue': 230 | ids = malpem_tissue_ids 231 | else: 232 | print("Method is not recognised. Exiting.") 233 | return -1 234 | 235 | if convert_to_protocol: 236 | # map from consecutive ints to protocol labels 237 | for i in range(len(ids)): 238 | out_lbl[lbl == i] = ids[i] 239 | else: 240 | # map from protocol labels to consecutive ints 241 | for i in range(len(ids)): 242 | out_lbl[lbl == ids[i]] = i 243 | 244 | return out_lbl 245 | 246 | 247 | def read_fn(file_references, mode, params=None): 248 | """A custom python read function for interfacing with nii image files. 249 | 250 | Args: 251 | file_references (list): A list of lists containing file references, 252 | such as [['id_0', 'image_filename_0', target_value_0], ..., 253 | ['id_N', 'image_filename_N', target_value_N]]. 254 | mode (str): One of the tf.estimator.ModeKeys strings: TRAIN, EVAL 255 | or PREDICT. 256 | params (dict, optional): A dictionary to parameterise read_fn ouputs 257 | (e.g. reader_params = {'n_examples': 10, 'example_size': 258 | [64, 64, 64], 'extract_examples': True}, etc.). 259 | 260 | Yields: 261 | dict: A dictionary of reader outputs for dltk.io.abstract_reader. 262 | """ 263 | 264 | if mode == tf.estimator.ModeKeys.TRAIN: 265 | np.random.shuffle(file_references) 266 | 267 | for f in file_references: 268 | 269 | # Read the image nii with sitk 270 | img_id = f[0] 271 | img_fn = f[1] 272 | img_sitk = sitk.ReadImage(str(img_fn)) 273 | img = sitk.GetArrayFromImage(img_sitk) 274 | 275 | # Normalise volume image 276 | img = whitening(img) 277 | 278 | # Create a 4D image (i.e. [x, y, z, channels]) 279 | img = np.expand_dims(img, axis=-1).astype(np.float32) 280 | 281 | if mode == tf.estimator.ModeKeys.PREDICT: 282 | yield {'features': {'x': img}, 283 | 'labels': None, 284 | 'sitk': img_sitk, 285 | 'img_id': img_id} 286 | continue 287 | 288 | # Read the label nii with sitk for each of the protocols 289 | lbls = [] 290 | for p in params['protocols']: 291 | idx = ALL_PROTOCOLS.index(p) 292 | lbl_fn = f[2 + idx] 293 | lbl = sitk.GetArrayFromImage(sitk.ReadImage(str(lbl_fn))).astype(np.int32) 294 | 295 | # Map the label ids to consecutive integers 296 | lbl = map_labels(lbl, protocol=p) 297 | lbls.append(lbl) 298 | 299 | # Check if the reader is supposed to return training examples or 300 | # full images 301 | if params['extract_examples']: 302 | # Concatenate into a list of images and labels and extract 303 | img_lbls_list = [img] + lbls 304 | img_lbls_list = extract_random_example_array( 305 | img_lbls_list, 306 | example_size=params['example_size'], 307 | n_examples=params['n_examples']) 308 | 309 | # Yield each image example and corresponding label protocols 310 | for e in range(params['n_examples']): 311 | yield {'features': {'x': img_lbls_list[0][e].astype(np.float32)}, 312 | 'labels': {params['protocols'][i]: img_lbls_list[1 + i][e] 313 | for i in range(len(params['protocols']))}} 314 | else: 315 | yield {'features': {'x': img}, 316 | 'labels': {params['protocols'][i]: 317 | lbls[i] for i in range(len(params['protocols']))}, 318 | 'sitk': img_sitk, 319 | 'img_id': img_id} 320 | return 321 | -------------------------------------------------------------------------------- /model/reader.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fitushar/Brain-Tissue-Segmentation-Using-Deep-Learning-Pipeline-NeuroNet/e3d33136d34a9ea4b55b3e210c78271980b683e2/model/reader.pyc -------------------------------------------------------------------------------- /model/reader2.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fitushar/Brain-Tissue-Segmentation-Using-Deep-Learning-Pipeline-NeuroNet/e3d33136d34a9ea4b55b3e210c78271980b683e2/model/reader2.pyc -------------------------------------------------------------------------------- /model/sandbox.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import tensorflow as tf\n", 10 | "import numpy as np\n", 11 | "import pandas as pd\n", 12 | "import json" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 100, 18 | "metadata": {}, 19 | "outputs": [ 20 | { 21 | "name": "stdout", 22 | "output_type": "stream", 23 | "text": [ 24 | "['4453610' '/vol/biobank/12579/brain/images/4453610/T1.nii.gz'\n", 25 | " '/vol/biobank/12579/brain/images/4453610/T1_brain_seg.nii.gz'\n", 26 | " '/vol/biobank/12579/brain/images/4453610/T1_first_all_fast_firstseg.nii.gz'\n", 27 | " '/vol/biobank/12579/brain/images/4453610/T1_brain_seg_spm.nii.gz'\n", 28 | " '/vol/biobank/12579/brain/images/4453610/T1_MALPEM.nii.gz'\n", 29 | " '/vol/biobank/12579/brain/images/4453610/T1_MALPEM_tissues.nii.gz']\n", 30 | "debug\n", 31 | "(64, 64, 64, 1)\n", 32 | "debug\n", 33 | "malp_em_tissue\n", 34 | "spm_tissue\n", 35 | "fsl_fast\n", 36 | "(64, 64, 64)\n", 37 | "(64, 64, 64)\n", 38 | "(64, 64, 64)\n" 39 | ] 40 | }, 41 | { 42 | "data": { 43 | "text/plain": [ 44 | "[None, None, None]" 45 | ] 46 | }, 47 | "execution_count": 100, 48 | "metadata": {}, 49 | "output_type": "execute_result" 50 | } 51 | ], 52 | "source": [ 53 | "import reader as reader\n", 54 | "import importlib\n", 55 | "importlib.reload(reader)\n", 56 | "\n", 57 | "all_filenames = pd.read_csv(\n", 58 | " 'train.csv',\n", 59 | " dtype=object,\n", 60 | " keep_default_na=False,\n", 61 | " na_values=[]).as_matrix()\n", 62 | "\n", 63 | "# Parse the run config\n", 64 | "with open(\"config_tissue.json\") as f:\n", 65 | " config = json.load(f)\n", 66 | " \n", 67 | "# Set up a data reader to handle the file i/o. \n", 68 | "reader_params = {\n", 69 | " 'n_examples': 16,\n", 70 | " 'example_size': [64, 64, 64],\n", 71 | " 'extract_examples': True,\n", 72 | " 'protocols' : config[\"protocols\"]}\n", 73 | "\n", 74 | "# Create a generator with the read file_references `all_filenames` and \n", 75 | "# `reader_params` in PREDICT mode:\n", 76 | "it = reader.read_fn(file_references=all_filenames,\n", 77 | " mode=tf.estimator.ModeKeys.TRAIN,\n", 78 | " params=reader_params)\n", 79 | "\n", 80 | "# If you call `next`, the `read_fn` will yield an output dictionary as designed\n", 81 | "# by you:\n", 82 | "ex_dict = next(it)\n", 83 | "\n", 84 | "# Print that output dict to debug\n", 85 | "np.set_printoptions(edgeitems=1)\n", 86 | "print('debug')\n", 87 | "print(ex_dict['features']['x'].shape)\n", 88 | "print('debug')\n", 89 | "[print(l) for l in ex_dict['labels']]\n", 90 | "[print(ex_dict['labels'][l].shape) for l in ex_dict['labels']]" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 93, 96 | "metadata": {}, 97 | "outputs": [ 98 | { 99 | "data": { 100 | "image/png": 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De+ty+xHtc8W55XGWMqTMd7Iuzesgr+H7qOt9o9U1bHZtVjN8GkwpC8u69Hyz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\nrRzzc5OUb5tlw/bsnOVel6P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101 | "text/plain": [ 102 | "" 103 | ] 104 | }, 105 | "metadata": {}, 106 | "output_type": "display_data" 107 | } 108 | ], 109 | "source": [ 110 | "# We can visualise the `batch_features` using matplotlib.\n", 111 | "%matplotlib inline\n", 112 | "import matplotlib.pyplot as plt\n", 113 | "\n", 114 | "f, axarr = plt.subplots(1, 3, figsize=(16,8))\n", 115 | "axarr[0].imshow(np.squeeze(ex_dict['features']['x'][32, :, :, 0]), cmap='gray')\n", 116 | "axarr[0].set_title('Input: x')\n", 117 | "axarr[0].axis('off')\n", 118 | "\n", 119 | "axarr[1].imshow(np.squeeze(ex_dict['labels']['spm_tissue'][32, :, :]), cmap='gray')\n", 120 | "axarr[1].set_title('spm_tissue')\n", 121 | "axarr[1].axis('off')\n", 122 | "\n", 123 | "axarr[2].imshow(np.squeeze(ex_dict['labels']['malp_em_tissue'][32, :, :]), cmap='gray')\n", 124 | "axarr[2].set_title('malp_em_tissue')\n", 125 | "axarr[2].axis('off')\n", 126 | "\n", 127 | "plt.show()" 128 | ] 129 | }, 130 | { 131 | "cell_type": "code", 132 | "execution_count": null, 133 | "metadata": {}, 134 | "outputs": [], 135 | "source": [] 136 | } 137 | ], 138 | "metadata": { 139 | "kernelspec": { 140 | "display_name": "Python 3", 141 | "language": "python", 142 | "name": "python3" 143 | }, 144 | "language_info": { 145 | "codemirror_mode": { 146 | "name": "ipython", 147 | "version": 3 148 | }, 149 | "file_extension": ".py", 150 | "mimetype": "text/x-python", 151 | "name": "python", 152 | "nbconvert_exporter": "python", 153 | "pygments_lexer": "ipython3", 154 | "version": "3.5.2" 155 | } 156 | }, 157 | "nbformat": 4, 158 | "nbformat_minor": 2 159 | } 160 | -------------------------------------------------------------------------------- /model/test.csv: -------------------------------------------------------------------------------- 1 | id,t1,spm_tissue 2 | 2,/home/maia_kbf/MISA_FIT/Preprocessing/Test_Set/IBSR_02.nii,/home/maia_kbf/MISA_FIT/Preprocessing/Test_Set/IBSR_02_seg.nii 3 | 10,/home/maia_kbf/MISA_FIT/Preprocessing/Test_Set/IBSR_10.nii,/home/maia_kbf/MISA_FIT/Preprocessing/Test_Set/IBSR_10_seg.nii 4 | 15,/home/maia_kbf/MISA_FIT/Preprocessing/Test_Set/IBSR_15.nii,/home/maia_kbf/MISA_FIT/Preprocessing/Test_Set/IBSR_15_seg.nii 5 | 11,/home/maia_kbf/MISA_FIT/Preprocessing/Validation_Set/IBSR_11.nii,/home/maia_kbf/MISA_FIT/Preprocessing/Validation_Set/IBSR_11_seg.nii 6 | 12,/home/maia_kbf/MISA_FIT/Preprocessing/Validation_Set/IBSR_12.nii,/home/maia_kbf/MISA_FIT/Preprocessing/Validation_Set/IBSR_12_seg.nii 7 | 13,/home/maia_kbf/MISA_FIT/Preprocessing/Validation_Set/IBSR_13.nii,/home/maia_kbf/MISA_FIT/Preprocessing/Validation_Set/IBSR_13_seg.nii 8 | 14,/home/maia_kbf/MISA_FIT/Preprocessing/Validation_Set/IBSR_14.nii,/home/maia_kbf/MISA_FIT/Preprocessing/Validation_Set/IBSR_14_seg.nii 9 | 17,/home/maia_kbf/MISA_FIT/Preprocessing/Validation_Set/IBSR_17.nii,/home/maia_kbf/MISA_FIT/Preprocessing/Validation_Set/IBSR_17_seg.nii 10 | 11 | -------------------------------------------------------------------------------- /model/train.csv: -------------------------------------------------------------------------------- 1 | id,subj_t1,subj_label 2 | IBSR_01,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_01.nii,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_01_seg.nii 3 | IBSR_03,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_03.nii,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_03_seg.nii 4 | IBSR_04,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_04.nii,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_04_seg.nii 5 | IBSR_05,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_05.nii,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_05_seg.nii 6 | IBSR_06,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_06.nii,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_06_seg.nii 7 | IBSR_07,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_07.nii,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_07_seg.nii 8 | IBSR_08,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_08.nii,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_08_seg.nii 9 | IBSR_09,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_09.nii,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_09_seg.nii 10 | IBSR_16,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_16.nii,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_16_seg.nii 11 | IBSR_18,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_18.nii,/home/maia_kbf/MISA_FIT/FinalData/TrainingSet/IBSR_18_seg.nii 12 | -------------------------------------------------------------------------------- /model/train.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | from __future__ import division 3 | from __future__ import print_function 4 | 5 | import argparse 6 | import os 7 | 8 | import numpy as np 9 | import pandas as pd 10 | import tensorflow as tf 11 | from keras import backend as K 12 | ####Tensorflow wizard 13 | config=tf.ConfigProto() 14 | config.gpu_options.allow_growth=True 15 | config.gpu_options.per_process_gpu_memory_fraction=0.23 16 | K.tensorflow_backend.set_session(tf.Session(config=config)) 17 | 18 | 19 | from dltk.core.metrics import dice 20 | from dltk.core.activations import leaky_relu 21 | from dltk.io.abstract_reader import Reader 22 | 23 | from neuronet import neuronet_3d 24 | 25 | from reader import read_fn 26 | import json 27 | 28 | # PARAMS 29 | EVAL_EVERY_N_STEPS = 100 30 | EVAL_STEPS = 10 31 | 32 | NUM_CHANNELS = 1 33 | 34 | BATCH_SIZE = 1 35 | SHUFFLE_CACHE_SIZE = 10 36 | 37 | MAX_STEPS = 4000 38 | 39 | 40 | # MODEL 41 | def model_fn(features, labels, mode, params): 42 | 43 | # 1. create a model and its outputs 44 | def lrelu(x): 45 | return leaky_relu(x, 0.1) 46 | 47 | protocols = params["protocols"] 48 | 49 | net_output_ops = neuronet_3d(features['x'], 50 | num_classes=params["num_classes"], 51 | protocols=protocols, 52 | num_res_units=params["network"]["num_residual_units"], 53 | filters=params["network"]["filters"], 54 | strides=params["network"]["strides"], 55 | activation=lrelu, 56 | mode=mode) 57 | 58 | # 1.1 Generate predictions only (for `ModeKeys.PREDICT`) 59 | if mode == tf.estimator.ModeKeys.PREDICT: 60 | return tf.estimator.EstimatorSpec( 61 | mode=mode, 62 | predictions=net_output_ops, 63 | export_outputs={'out': tf.estimator.export.PredictOutput(net_output_ops)}) 64 | 65 | # 2. set up a loss function 66 | ce = [] 67 | for p in protocols: 68 | ce.append(tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( 69 | logits=net_output_ops['logits_{}'.format(p)], 70 | labels=labels[p]))) 71 | 72 | # Sum the crossentropy losses and divide through number of protocols to be predicted 73 | loss = tf.div(tf.add_n(ce), tf.constant(len(protocols), dtype=tf.float32)) 74 | 75 | # 3. define a training op and ops for updating moving averages (i.e. for batch normalisation) 76 | global_step = tf.train.get_global_step() 77 | optimiser = tf.train.AdamOptimizer(learning_rate=params["learning_rate"], epsilon=1e-5) 78 | 79 | update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) 80 | with tf.control_dependencies(update_ops): 81 | train_op = optimiser.minimize(loss, global_step=global_step) 82 | 83 | # 4.1 (optional) create custom image summaries for tensorboard 84 | my_image_summaries = {} 85 | my_image_summaries['feat_t1'] = features['x'][0, 64, :, :, 0] 86 | for p in protocols: 87 | my_image_summaries['{}/lbl'.format(p)] = tf.cast(labels[p], tf.float32)[0, 64, :, :] 88 | my_image_summaries['{}/pred'.format(p)] = tf.cast(net_output_ops['y_{}'.format(p)], tf.float32)[0, 64, :, :] 89 | 90 | expected_output_size = [1, 128, 128, 1] # [B, W, H, C] 91 | [tf.summary.image(name, tf.reshape(image, expected_output_size)) 92 | for name, image in my_image_summaries.items()] 93 | 94 | # 4.2 (optional) create custom metric summaries for tensorboard 95 | for i in range(len(protocols)): 96 | p = protocols[i] 97 | c = tf.constant(params["num_classes"][i]) 98 | 99 | mean_dice = tf.reduce_mean(tf.py_func( 100 | dice, [net_output_ops['y_{}'.format(p)], labels[p], c], tf.float32)[1:]) 101 | tf.summary.scalar('dsc_{}'.format(p), mean_dice) 102 | 103 | # 5. Return EstimatorSpec object 104 | return tf.estimator.EstimatorSpec(mode=mode, 105 | predictions=None, 106 | loss=loss, 107 | train_op=train_op, 108 | eval_metric_ops=None) 109 | 110 | 111 | def train(args, config): 112 | 113 | np.random.seed(42) 114 | tf.set_random_seed(42) 115 | 116 | print('Setting up...') 117 | # Parse csv files for file names 118 | train_filenames = pd.read_csv(args.train_csv, 119 | dtype=object, 120 | keep_default_na=False, 121 | na_values=[]).as_matrix() 122 | 123 | val_filenames = pd.read_csv(args.val_csv, 124 | dtype=object, 125 | keep_default_na=False, 126 | na_values=[]).as_matrix() 127 | 128 | # Set up a data reader to handle the file i/o. 129 | reader_params = { 130 | 'n_examples':5, 131 | 'example_size': [128, 128, 128], 132 | 'extract_examples': True, 133 | 'protocols': config["protocols"]} 134 | 135 | reader_example_shapes = { 136 | 'features': {'x': reader_params['example_size'] + [NUM_CHANNELS, ]}, 137 | 'labels': {p: reader_params['example_size'] for p in config["protocols"]}} 138 | 139 | reader = Reader(read_fn, 140 | {'features': {'x': tf.float32}, 141 | 'labels': {p: tf.int32 for p in config["protocols"]}}) 142 | 143 | # Get input functions and queue initialisation hooks for training and validation data 144 | train_input_fn, train_qinit_hook = reader.get_inputs( 145 | train_filenames, 146 | tf.estimator.ModeKeys.TRAIN, 147 | example_shapes=reader_example_shapes, 148 | batch_size=BATCH_SIZE, 149 | shuffle_cache_size=SHUFFLE_CACHE_SIZE, 150 | params=reader_params) 151 | 152 | val_input_fn, val_qinit_hook = reader.get_inputs( 153 | val_filenames, 154 | tf.estimator.ModeKeys.EVAL, 155 | example_shapes=reader_example_shapes, 156 | batch_size=BATCH_SIZE, 157 | shuffle_cache_size=SHUFFLE_CACHE_SIZE, 158 | params=reader_params) 159 | 160 | # Instantiate the neural network estimator 161 | nn = tf.estimator.Estimator(model_fn=model_fn, 162 | model_dir=config["model_path"], 163 | params=config, 164 | config=tf.estimator.RunConfig(session_config=tf.ConfigProto())) 165 | 166 | # Hooks for validation summaries 167 | val_summary_hook = tf.contrib.training.SummaryAtEndHook( 168 | os.path.join(config["model_path"], 'eval')) 169 | step_cnt_hook = tf.train.StepCounterHook( 170 | every_n_steps=EVAL_EVERY_N_STEPS, output_dir=config["model_path"]) 171 | 172 | print('Starting training...') 173 | try: 174 | for _ in range(MAX_STEPS // EVAL_EVERY_N_STEPS): 175 | nn.train(input_fn=train_input_fn, 176 | hooks=[train_qinit_hook, step_cnt_hook], 177 | steps=EVAL_EVERY_N_STEPS) 178 | 179 | results_val = nn.evaluate(input_fn=val_input_fn, 180 | hooks=[val_qinit_hook, val_summary_hook], 181 | steps=EVAL_STEPS) 182 | print('Step = {}; val loss = {:.5f};'.format(results_val['global_step'], results_val['loss'])) 183 | 184 | except KeyboardInterrupt: 185 | pass 186 | 187 | print('Stopping now.') 188 | export_dir = nn.export_savedmodel( 189 | export_dir_base=config["model_path"], 190 | serving_input_receiver_fn=reader.serving_input_receiver_fn(reader_example_shapes)) 191 | print('Model saved to {}.'.format(export_dir)) 192 | 193 | 194 | if __name__ == '__main__': 195 | 196 | # Set up argument parser 197 | parser = argparse.ArgumentParser(description='NeuroNet training script') 198 | parser.add_argument('--restart', default=False, action='store_true') 199 | parser.add_argument('--verbose', default=False, action='store_true') 200 | parser.add_argument('--cuda_devices', '-c', default='0') 201 | 202 | parser.add_argument('--train_csv', default='train.csv') 203 | parser.add_argument('--val_csv', default='val.csv') 204 | parser.add_argument('--config', default='config_all.json') 205 | 206 | args = parser.parse_args() 207 | 208 | # Set verbosity 209 | if args.verbose: 210 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1' 211 | tf.logging.set_verbosity(tf.logging.INFO) 212 | else: 213 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' 214 | tf.logging.set_verbosity(tf.logging.ERROR) 215 | 216 | # GPU allocation options 217 | os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda_devices 218 | 219 | # Parse the run config 220 | with open(args.config) as f: 221 | config = json.load(f) 222 | 223 | # Handle restarting and resuming training 224 | if args.restart: 225 | print('Restarting training from scratch.') 226 | os.system('rm -rf {}'.format(config["model_path"])) 227 | 228 | if not os.path.isdir(config["model_path"]): 229 | os.system('mkdir -p {}'.format(config["model_path"])) 230 | else: 231 | print('Resuming training on model_path {}'.format(config["model_path"])) 232 | 233 | # Call training 234 | train(args, config) 235 | -------------------------------------------------------------------------------- /model/val.csv: -------------------------------------------------------------------------------- 1 | id,subj_t1,subj_label 2 | IBSR_11,/home/maia_kbf/MISA_FIT/FinalData/ValidationSet/IBSR_11.nii,/home/maia_kbf/MISA_FIT/FinalData/ValidationSet/IBSR_11_seg.nii 3 | IBSR_12,/home/maia_kbf/MISA_FIT/FinalData/ValidationSet/IBSR_12.nii,/home/maia_kbf/MISA_FIT/FinalData/ValidationSet/IBSR_12_seg.nii 4 | IBSR_13,/home/maia_kbf/MISA_FIT/FinalData/ValidationSet/IBSR_13.nii,/home/maia_kbf/MISA_FIT/FinalData/ValidationSet/IBSR_13_seg.nii 5 | IBSR_14,/home/maia_kbf/MISA_FIT/FinalData/ValidationSet/IBSR_14.nii,/home/maia_kbf/MISA_FIT/FinalData/ValidationSet/IBSR_14_seg.nii 6 | IBSR_17,/home/maia_kbf/MISA_FIT/FinalData/ValidationSet/IBSR_17.nii,/home/maia_kbf/MISA_FIT/FinalData/ValidationSet/IBSR_17_seg.nii 7 | -------------------------------------------------------------------------------- /paper_147.pptx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fitushar/Brain-Tissue-Segmentation-Using-Deep-Learning-Pipeline-NeuroNet/e3d33136d34a9ea4b55b3e210c78271980b683e2/paper_147.pptx --------------------------------------------------------------------------------