├── LICENSE ├── README.md ├── challegevnet.jpg ├── gt_0_epoch.png ├── gt_10000_epoch.png ├── gt_1000_epoch.png ├── leadboard9.PNG ├── loss.PNG ├── mask_15_epoch.png ├── predict_0_epoch.png ├── predict_10000_epoch.png ├── predict_1000_epoch.png ├── promise12_header.png ├── promise2012 ├── Vnet │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-35.pyc │ │ ├── layer.cpython-35.pyc │ │ ├── model_Densevnet3d.cpython-35.pyc │ │ └── model_vnet3d.cpython-35.pyc │ ├── layer.py │ ├── model_vnet3d.py │ └── util.py ├── promise12Vnet3dImage.csv ├── promise12Vnet3dMask.csv └── vnet3d_train_predict.py ├── src_15_epoch.png └── vnet.PNG /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 junqiangchen 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ImageSegmentation With Vnet3D 2 | > This is an example of the prostate in transversal T2-weighted MR images Segment from MICCAI Grand Challenge:Prostate MR Image Segmentation 2012 3 | ![](promise12_header.png) 4 | 5 | ## Prerequisities 6 | The following dependencies are needed: 7 | - numpy >= 1.11.1 8 | - SimpleITK >=1.0.1 9 | - opencv-python >=3.3.0 10 | - tensorflow-gpu ==1.8.0 11 | - pandas >=0.20.1 12 | - scikit-learn >= 0.17.1 13 | 14 | ## How to Use 15 | (re)implemented the model with tensorflow in the paper of "Milletari, F., Navab, N., & Ahmadi, S. A. (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation.3DV 2016" 16 | 17 | **1、download trained data,download dataset：https://promise12.grand-challenge.org/download/ ,if you can't download it,i have shared it:https://pan.baidu.com/s/1y9YAAQKdD3OMOMyamx9MdA, password:whbf** 18 | 19 | **2、the file of promise12Vnet3dImage.csv,is like this format: 20 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_10 21 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_11 22 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_12 23 | ...... 24 | if you trained data path is not D:\Data\PROMISE2012\,you should change the csv file path just like this:using C:\Data\ replace D:\Data\PROMISE2012\.** 25 | 26 | **3、when data is prepared,just run the vnet3d_train_predict.py** 27 | 28 | **4、training the model on the GTX1080,it take 40 hours,and i also attach the trained model in the project,you also just use the vnet3d_train_predict.py file to predict,and get the segmentation result.** 29 | 30 | **5、download trained model:https://pan.baidu.com/s/1kQ1SCVuBK6xJFR7cyKN7XQ password:0ytv** 31 | 32 | **6、download test data: https://pan.baidu.com/s/1pDCQzTxUmyYdwDinBJKTuA, password:s0jt** 33 | 34 | ## Result 35 | MICCAI Grand Challenge Result 36 | ![](leadboard9.PNG) 37 | 38 | the trained loss result 39 | ![](loss.PNG) 40 | the Vnet3D model 41 | ![](vnet.PNG) 42 | the trained process:0 epoch——GTMask and PredictMask 43 | ![](gt_0_epoch.png) 44 | ![](predict_0_epoch.png) 45 | 1000 epoch——GTMask and PredictMask 46 | ![](gt_1000_epoch.png) 47 | ![](predict_1000_epoch.png) 48 | 10000 epoch——GTMask and PredictMask 49 | ![](gt_10000_epoch.png) 50 | ![](predict_10000_epoch.png) 51 | the predict result 52 | ![](mask_15_epoch.png) 53 | ![](src_15_epoch.png) 54 | ## Contact 55 | * https://github.com/junqiangchen 56 | * email: 1207173174@qq.com 57 | * WeChat Public number: 最新医学影像技术 58 | 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https://raw.githubusercontent.com/junqiangchen/VNet3D/e8a121a28270b7503eaf1fda85dbf4bd18f9b845/promise2012/Vnet/__pycache__/__init__.cpython-35.pyc -------------------------------------------------------------------------------- /promise2012/Vnet/__pycache__/layer.cpython-35.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/junqiangchen/VNet3D/e8a121a28270b7503eaf1fda85dbf4bd18f9b845/promise2012/Vnet/__pycache__/layer.cpython-35.pyc -------------------------------------------------------------------------------- /promise2012/Vnet/__pycache__/model_Densevnet3d.cpython-35.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/junqiangchen/VNet3D/e8a121a28270b7503eaf1fda85dbf4bd18f9b845/promise2012/Vnet/__pycache__/model_Densevnet3d.cpython-35.pyc -------------------------------------------------------------------------------- /promise2012/Vnet/__pycache__/model_vnet3d.cpython-35.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/junqiangchen/VNet3D/e8a121a28270b7503eaf1fda85dbf4bd18f9b845/promise2012/Vnet/__pycache__/model_vnet3d.cpython-35.pyc -------------------------------------------------------------------------------- /promise2012/Vnet/layer.py: -------------------------------------------------------------------------------- 1 | ''' 2 | covlution layer，pool layer，initialization。。。。 3 | ''' 4 | import tensorflow as tf 5 | import numpy as np 6 | import cv2 7 | 8 | 9 | # Weight initialization (Xavier's init) 10 | def weight_xavier_init(shape, n_inputs, n_outputs, activefunction='sigmoid', uniform=True, variable_name=None): 11 | with tf.device('/cpu:0'): 12 | if activefunction == 'sigmoid': 13 | if uniform: 14 | init_range = tf.sqrt(6.0 / (n_inputs + n_outputs)) 15 | initial = tf.random_uniform(shape, -init_range, init_range) 16 | return tf.get_variable(initializer=initial, trainable=True, name=variable_name) 17 | else: 18 | stddev = tf.sqrt(2.0 / (n_inputs + n_outputs)) 19 | initial = tf.truncated_normal(shape, mean=0.0, stddev=stddev) 20 | return tf.get_variable(initializer=initial, trainable=True, name=variable_name) 21 | elif activefunction == 'relu': 22 | if uniform: 23 | init_range = tf.sqrt(6.0 / (n_inputs + n_outputs)) * np.sqrt(2) 24 | initial = tf.random_uniform(shape, -init_range, init_range) 25 | return tf.get_variable(initializer=initial, trainable=True, name=variable_name) 26 | else: 27 | stddev = tf.sqrt(2.0 / (n_inputs + n_outputs)) * np.sqrt(2) 28 | initial = tf.truncated_normal(shape, mean=0.0, stddev=stddev) 29 | return tf.get_variable(initializer=initial, trainable=True, name=variable_name) 30 | elif activefunction == 'tan': 31 | if uniform: 32 | init_range = tf.sqrt(6.0 / (n_inputs + n_outputs)) * 4 33 | initial = tf.random_uniform(shape, -init_range, init_range) 34 | return tf.get_variable(initializer=initial, trainable=True, name=variable_name) 35 | else: 36 | stddev = tf.sqrt(2.0 / (n_inputs + n_outputs)) * 4 37 | initial = tf.truncated_normal(shape, mean=0.0, stddev=stddev) 38 | return tf.get_variable(initializer=initial, trainable=True, name=variable_name) 39 | 40 | 41 | # Bias initialization 42 | def bias_variable(shape, variable_name=None): 43 | with tf.device('/cpu:0'): 44 | initial = tf.constant(0.1, shape=shape) 45 | return tf.get_variable(initializer=initial, trainable=True, name=variable_name) 46 | 47 | 48 | # 3D convolution 49 | def conv3d(x, W, stride=1): 50 | conv_3d = tf.nn.conv3d(x, W, strides=[1, stride, stride, stride, 1], padding='SAME') 51 | return conv_3d 52 | 53 | 54 | # 3D deconvolution 55 | 56 | def deconv3d(x, W, depth=False): 57 | """ 58 | depth flag:False is z axis is same between input and output,true is z axis is input is twice than output 59 | """ 60 | x_shape = tf.shape(x) 61 | if depth: 62 | output_shape = tf.stack([x_shape[0], x_shape[1] * 2, x_shape[2] * 2, x_shape[3] * 2, x_shape[4] // 2]) 63 | deconv = tf.nn.conv3d_transpose(x, W, output_shape, strides=[1, 2, 2, 2, 1], padding='SAME') 64 | else: 65 | output_shape = tf.stack([x_shape[0], x_shape[1] * 2, x_shape[2] * 2, x_shape[3], x_shape[4] // 2]) 66 | deconv = tf.nn.conv3d_transpose(x, W, output_shape, strides=[1, 2, 2, 1, 1], padding='SAME') 67 | return deconv 68 | 69 | 70 | # Max Pooling 71 | def max_pool3d(x, depth=False): 72 | """ 73 | depth flag:False is z axis is same between input and output,true is z axis is input is twice than output 74 | """ 75 | if depth: 76 | pool3d = tf.nn.max_pool3d(x, ksize=[1, 2, 2, 2, 1], strides=[1, 2, 2, 2, 1], padding='SAME') 77 | else: 78 | pool3d = tf.nn.max_pool3d(x, ksize=[1, 2, 2, 1, 1], strides=[1, 2, 2, 1, 1], padding='SAME') 79 | return pool3d 80 | 81 | 82 | # Unet crop and concat 83 | def crop_and_concat(x1, x2): 84 | x1_shape = tf.shape(x1) 85 | x2_shape = tf.shape(x2) 86 | # offsets for the top left corner of the crop 87 | offsets = [0, (x1_shape[1] - x2_shape[1]) // 2, 88 | (x1_shape[2] - x2_shape[2]) // 2, (x1_shape[3] - x2_shape[3]) // 2, 0] 89 | size = [-1, x2_shape[1], x2_shape[2], x2_shape[3], -1] 90 | x1_crop = tf.slice(x1, offsets, size) 91 | return tf.concat([x1_crop, x2], 4) 92 | 93 | 94 | # Batch Normalization 95 | def normalizationlayer(x, is_train, height=None, width=None, image_z=None, norm_type=None, G=16, esp=1e-5, scope=None): 96 | """ 97 | :param x:input data with shap of[batch,height,width,channel] 98 | :param is_train:flag of normalizationlayer,True is training,False is Testing 99 | :param height:in some condition,the data height is in Runtime determined,such as through deconv layer and conv2d 100 | :param width:in some condition,the data width is in Runtime determined 101 | :param image_z: 102 | :param norm_type:normalization type:support"batch","group","None" 103 | :param G:in group normalization,channel is seperated with group number(G) 104 | :param esp:Prevent divisor from being zero 105 | :param scope:normalizationlayer scope 106 | :return: 107 | """ 108 | with tf.name_scope(scope + norm_type): 109 | if norm_type == None: 110 | output = x 111 | elif norm_type == 'batch': 112 | output = tf.contrib.layers.batch_norm(x, center=True, scale=True, is_train=is_train) 113 | elif norm_type == "group": 114 | # tranpose:[bs,z,h,w,c]to[bs,c,z,h,w]following the paper 115 | x = tf.transpose(x, [0, 4, 1, 2, 3]) 116 | N, C, Z, H, W = x.get_shape().as_list() 117 | G = min(G, C) 118 | if H == None and W == None and Z == None: 119 | Z, H, W = image_z, height, width 120 | x = tf.reshape(x, [-1, G, C // G, Z, H, W]) 121 | mean, var = tf.nn.moments(x, [2, 3, 4, 5], keep_dims=True) 122 | x = (x - mean) / tf.sqrt(var + esp) 123 | gama = tf.get_variable(scope + norm_type + 'group_gama', [C], initializer=tf.constant_initializer(1.0)) 124 | beta = tf.get_variable(scope + norm_type + 'group_beta', [C], initializer=tf.constant_initializer(0.0)) 125 | gama = tf.reshape(gama, [1, C, 1, 1, 1]) 126 | beta = tf.reshape(beta, [1, C, 1, 1, 1]) 127 | output = tf.reshape(x, [-1, C, Z, H, W]) * gama + beta 128 | # tranpose:[bs,c,z,h,w]to[bs,z,h,w,c]following the paper 129 | output = tf.transpose(output, [0, 2, 3, 4, 1]) 130 | return output 131 | 132 | 133 | # resnet add_connect 134 | def resnet_Add(x1, x2): 135 | if x1.get_shape().as_list()[4] != x2.get_shape().as_list()[4]: 136 | # Option A: Zero-padding 137 | residual_connection = x2 + tf.pad(x1, [[0, 0], [0, 0], [0, 0], [0, 0], 138 | [0, x2.get_shape().as_list()[4] - 139 | x1.get_shape().as_list()[4]]]) 140 | else: 141 | residual_connection = x2 + x1 142 | return residual_connection 143 | 144 | 145 | def save_images(images, size, path): 146 | img = (images + 1.0) / 2.0 147 | h, w = img.shape[1], img.shape[2] 148 | merge_img = np.zeros((h * size[0], w * size[1])) 149 | for idx, image in enumerate(images): 150 | i = idx % size[1] 151 | j = idx // size[1] 152 | merge_img[j * h:j * h + h, i * w:i * w + w] = image 153 | result = merge_img * 255. 154 | result = np.clip(result, 0, 255).astype('uint8') 155 | return cv2.imwrite(path, result) 156 | -------------------------------------------------------------------------------- /promise2012/Vnet/model_vnet3d.py: -------------------------------------------------------------------------------- 1 | ''' 2 | 3 | ''' 4 | from Vnet.layer import (conv3d, deconv3d, normalizationlayer, crop_and_concat, resnet_Add, 5 | weight_xavier_init, bias_variable, save_images) 6 | import tensorflow as tf 7 | import numpy as np 8 | import cv2 9 | import os 10 | 11 | 12 | def conv_bn_relu_drop(x, kernal, phase, drop, image_z=None, height=None, width=None, scope=None): 13 | with tf.name_scope(scope): 14 | W = weight_xavier_init(shape=kernal, n_inputs=kernal[0] * kernal[1] * kernal[2] * kernal[3], 15 | n_outputs=kernal[-1], activefunction='relu', variable_name=scope + 'conv_W') 16 | B = bias_variable([kernal[-1]], variable_name=scope + 'conv_B') 17 | conv = conv3d(x, W) + B 18 | conv = normalizationlayer(conv, is_train=phase, height=height, width=width, image_z=image_z, norm_type='group', 19 | scope=scope) 20 | conv = tf.nn.dropout(tf.nn.relu(conv), drop) 21 | return conv 22 | 23 | 24 | def down_sampling(x, kernal, phase, drop, image_z=None, height=None, width=None, scope=None): 25 | with tf.name_scope(scope): 26 | W = weight_xavier_init(shape=kernal, n_inputs=kernal[0] * kernal[1] * kernal[2] * kernal[3], 27 | n_outputs=kernal[-1], 28 | activefunction='relu', variable_name=scope + 'W') 29 | B = bias_variable([kernal[-1]], variable_name=scope + 'B') 30 | conv = conv3d(x, W, 2) + B 31 | conv = normalizationlayer(conv, is_train=phase, height=height, width=width, image_z=image_z, norm_type='group', 32 | scope=scope) 33 | conv = tf.nn.dropout(tf.nn.relu(conv), drop) 34 | return conv 35 | 36 | 37 | def deconv_relu(x, kernal, scope=None): 38 | with tf.name_scope(scope): 39 | W = weight_xavier_init(shape=kernal, n_inputs=kernal[0] * kernal[1] * kernal[2] * kernal[-1], 40 | n_outputs=kernal[-2], activefunction='relu', variable_name=scope + 'W') 41 | B = bias_variable([kernal[-2]], variable_name=scope + 'B') 42 | conv = deconv3d(x, W, True) + B 43 | conv = tf.nn.relu(conv) 44 | return conv 45 | 46 | 47 | def conv_sigmod(x, kernal, scope=None): 48 | with tf.name_scope(scope): 49 | W = weight_xavier_init(shape=kernal, n_inputs=kernal[0] * kernal[1] * kernal[2] * kernal[3], 50 | n_outputs=kernal[-1], activefunction='sigmoid', variable_name=scope + 'W') 51 | B = bias_variable([kernal[-1]], variable_name=scope + 'B') 52 | conv = conv3d(x, W) + B 53 | conv = tf.nn.sigmoid(conv) 54 | return conv 55 | 56 | 57 | def _create_conv_net(X, image_z, image_width, image_height, image_channel, phase, drop, n_class=1): 58 | inputX = tf.reshape(X, [-1, image_z, image_width, image_height, image_channel]) # shape=(?, 32, 32, 1) 59 | # Vnet model 60 | # layer1->convolution 61 | layer0 = conv_bn_relu_drop(x=inputX, kernal=(3, 3, 3, image_channel, 16), phase=phase, drop=drop, 62 | scope='layer0') 63 | layer1 = conv_bn_relu_drop(x=layer0, kernal=(3, 3, 3, 16, 16), phase=phase, drop=drop, 64 | scope='layer1') 65 | layer1 = resnet_Add(x1=layer0, x2=layer1) 66 | # down sampling1 67 | down1 = down_sampling(x=layer1, kernal=(3, 3, 3, 16, 32), phase=phase, drop=drop, scope='down1') 68 | # layer2->convolution 69 | layer2 = conv_bn_relu_drop(x=down1, kernal=(3, 3, 3, 32, 32), phase=phase, drop=drop, 70 | scope='layer2_1') 71 | layer2 = conv_bn_relu_drop(x=layer2, kernal=(3, 3, 3, 32, 32), phase=phase, drop=drop, 72 | scope='layer2_2') 73 | layer2 = resnet_Add(x1=down1, x2=layer2) 74 | # down sampling2 75 | down2 = down_sampling(x=layer2, kernal=(3, 3, 3, 32, 64), phase=phase, drop=drop, scope='down2') 76 | # layer3->convolution 77 | layer3 = conv_bn_relu_drop(x=down2, kernal=(3, 3, 3, 64, 64), phase=phase, drop=drop, 78 | scope='layer3_1') 79 | layer3 = conv_bn_relu_drop(x=layer3, kernal=(3, 3, 3, 64, 64), phase=phase, drop=drop, 80 | scope='layer3_2') 81 | layer3 = conv_bn_relu_drop(x=layer3, kernal=(3, 3, 3, 64, 64), phase=phase, drop=drop, 82 | scope='layer3_3') 83 | layer3 = resnet_Add(x1=down2, x2=layer3) 84 | # down sampling3 85 | down3 = down_sampling(x=layer3, kernal=(3, 3, 3, 64, 128), phase=phase, drop=drop, scope='down3') 86 | # layer4->convolution 87 | layer4 = conv_bn_relu_drop(x=down3, kernal=(3, 3, 3, 128, 128), phase=phase, drop=drop, 88 | scope='layer4_1') 89 | layer4 = conv_bn_relu_drop(x=layer4, kernal=(3, 3, 3, 128, 128), phase=phase, drop=drop, 90 | scope='layer4_2') 91 | layer4 = conv_bn_relu_drop(x=layer4, kernal=(3, 3, 3, 128, 128), phase=phase, drop=drop, 92 | scope='layer4_3') 93 | layer4 = resnet_Add(x1=down3, x2=layer4) 94 | # down sampling4 95 | down4 = down_sampling(x=layer4, kernal=(3, 3, 3, 128, 256), phase=phase, drop=drop, scope='down4') 96 | # layer5->convolution 97 | layer5 = conv_bn_relu_drop(x=down4, kernal=(3, 3, 3, 256, 256), phase=phase, drop=drop, 98 | scope='layer5_1') 99 | layer5 = conv_bn_relu_drop(x=layer5, kernal=(3, 3, 3, 256, 256), phase=phase, drop=drop, 100 | scope='layer5_2') 101 | layer5 = conv_bn_relu_drop(x=layer5, kernal=(3, 3, 3, 256, 256), phase=phase, drop=drop, 102 | scope='layer5_3') 103 | layer5 = resnet_Add(x1=down4, x2=layer5) 104 | 105 | # layer9->deconvolution 106 | deconv1 = deconv_relu(x=layer5, kernal=(3, 3, 3, 128, 256), scope='deconv1') 107 | # layer8->convolution 108 | layer6 = crop_and_concat(layer4, deconv1) 109 | _, Z, H, W, _ = layer4.get_shape().as_list() 110 | layer6 = conv_bn_relu_drop(x=layer6, kernal=(3, 3, 3, 256, 128), image_z=Z, height=H, width=W, phase=phase, 111 | drop=drop, scope='layer6_1') 112 | layer6 = conv_bn_relu_drop(x=layer6, kernal=(3, 3, 3, 128, 128), image_z=Z, height=H, width=W, phase=phase, 113 | drop=drop, scope='layer6_2') 114 | layer6 = conv_bn_relu_drop(x=layer6, kernal=(3, 3, 3, 128, 128), image_z=Z, height=H, width=W, phase=phase, 115 | drop=drop, scope='layer6_3') 116 | layer6 = resnet_Add(x1=deconv1, x2=layer6) 117 | # layer9->deconvolution 118 | deconv2 = deconv_relu(x=layer6, kernal=(3, 3, 3, 64, 128), scope='deconv2') 119 | # layer8->convolution 120 | layer7 = crop_and_concat(layer3, deconv2) 121 | _, Z, H, W, _ = layer3.get_shape().as_list() 122 | layer7 = conv_bn_relu_drop(x=layer7, kernal=(3, 3, 3, 128, 64), image_z=Z, height=H, width=W, phase=phase, 123 | drop=drop, scope='layer7_1') 124 | layer7 = conv_bn_relu_drop(x=layer7, kernal=(3, 3, 3, 64, 64), image_z=Z, height=H, width=W, phase=phase, 125 | drop=drop, scope='layer7_2') 126 | layer7 = resnet_Add(x1=deconv2, x2=layer7) 127 | # layer9->deconvolution 128 | deconv3 = deconv_relu(x=layer7, kernal=(3, 3, 3, 32, 64), scope='deconv3') 129 | # layer8->convolution 130 | layer8 = crop_and_concat(layer2, deconv3) 131 | _, Z, H, W, _ = layer2.get_shape().as_list() 132 | layer8 = conv_bn_relu_drop(x=layer8, kernal=(3, 3, 3, 64, 32), image_z=Z, height=H, width=W, phase=phase, 133 | drop=drop, scope='layer10_1') 134 | layer8 = conv_bn_relu_drop(x=layer8, kernal=(3, 3, 3, 32, 32), image_z=Z, height=H, width=W, phase=phase, 135 | drop=drop, scope='layer10_2') 136 | layer8 = conv_bn_relu_drop(x=layer8, kernal=(3, 3, 3, 32, 32), image_z=Z, height=H, width=W, phase=phase, 137 | drop=drop, scope='layer10_3') 138 | layer8 = resnet_Add(x1=deconv3, x2=layer8) 139 | # layer9->deconvolution 140 | deconv4 = deconv_relu(x=layer8, kernal=(3, 3, 3, 16, 32), scope='deconv4') 141 | # layer8->convolution 142 | layer9 = crop_and_concat(layer1, deconv4) 143 | _, Z, H, W, _ = layer1.get_shape().as_list() 144 | layer9 = conv_bn_relu_drop(x=layer9, kernal=(3, 3, 3, 32, 32), image_z=Z, height=H, width=W, phase=phase, 145 | drop=drop, scope='layer11_1') 146 | layer9 = conv_bn_relu_drop(x=layer9, kernal=(3, 3, 3, 32, 32), image_z=Z, height=H, width=W, phase=phase, 147 | drop=drop, scope='layer11_2') 148 | layer9 = conv_bn_relu_drop(x=layer9, kernal=(3, 3, 3, 32, 32), image_z=Z, height=H, width=W, phase=phase, 149 | drop=drop, scope='layer11_3') 150 | layer9 = resnet_Add(x1=deconv4, x2=layer9) 151 | # layer14->output 152 | output_map = conv_sigmod(x=layer9, kernal=(1, 1, 1, 32, n_class), scope='output') 153 | return output_map 154 | 155 | 156 | # Serve data by batches 157 | def _next_batch(train_images, train_labels, batch_size, index_in_epoch): 158 | start = index_in_epoch 159 | index_in_epoch += batch_size 160 | 161 | num_examples = train_images.shape[0] 162 | # when all trainig data have been already used, it is reorder randomly 163 | if index_in_epoch > num_examples: 164 | # shuffle the data 165 | perm = np.arange(num_examples) 166 | np.random.shuffle(perm) 167 | train_images = train_images[perm] 168 | train_labels = train_labels[perm] 169 | # start next epoch 170 | start = 0 171 | index_in_epoch = batch_size 172 | assert batch_size <= num_examples 173 | end = index_in_epoch 174 | return train_images[start:end], train_labels[start:end], index_in_epoch 175 | 176 | 177 | class Vnet3dModule(object): 178 | """ 179 | A unet2d implementation 180 | 181 | :param image_height: number of height in the input image 182 | :param image_width: number of width in the input image 183 | :param image_depth: number of depth in the input image 184 | :param channels: number of channels in the input image 185 | :param costname: name of the cost function.Default is "dice coefficient" 186 | """ 187 | 188 | def __init__(self, image_height, image_width, image_depth, channels=1, costname="dice coefficient", inference=False, 189 | model_path=None): 190 | self.image_width = image_width 191 | self.image_height = image_height 192 | self.image_depth = image_depth 193 | self.channels = channels 194 | 195 | self.X = tf.placeholder("float", shape=[None, self.image_depth, self.image_height, self.image_width, 196 | self.channels]) 197 | self.Y_gt = tf.placeholder("float", shape=[None, self.image_depth, self.image_height, self.image_width, 198 | self.channels]) 199 | self.lr = tf.placeholder('float') 200 | self.phase = tf.placeholder(tf.bool) 201 | self.drop = tf.placeholder('float') 202 | 203 | self.Y_pred = _create_conv_net(self.X, self.image_depth, self.image_width, self.image_height, self.channels, 204 | self.phase, self.drop) 205 | self.cost = self.__get_cost(costname) 206 | self.accuracy = -self.__get_cost(costname) 207 | self.global_epoch = 0 208 | 209 | if inference: 210 | self.restore_training_2(model_path) 211 | else: 212 | n_epoch = self.restore_training_2(model_path) 213 | if n_epoch: 214 | self.global_epoch = self.global_epoch + n_epoch 215 | 216 | def __get_cost(self, cost_name): 217 | Z, H, W, C = self.Y_gt.get_shape().as_list()[1:] 218 | if cost_name == "dice coefficient": 219 | smooth = 1e-5 220 | pred_flat = tf.reshape(self.Y_pred, [-1, H * W * C * Z]) 221 | true_flat = tf.reshape(self.Y_gt, [-1, H * W * C * Z]) 222 | intersection = 2 * tf.reduce_sum(pred_flat * true_flat, axis=1) + smooth 223 | denominator = tf.reduce_sum(pred_flat, axis=1) + tf.reduce_sum(true_flat, axis=1) + smooth 224 | loss = -tf.reduce_mean(intersection / denominator) 225 | return loss 226 | 227 | def train(self, train_images, train_labels, model_path, logs_path, learning_rate, 228 | dropout_conv=0.8, train_epochs=10000, batch_size=1): 229 | train_op = tf.train.AdamOptimizer(self.lr).minimize(self.cost) 230 | 231 | init = tf.global_variables_initializer() 232 | saver = tf.train.Saver(max_to_keep=2) 233 | 234 | tf.summary.scalar("loss", self.cost) 235 | tf.summary.scalar("accuracy", self.accuracy) 236 | merged_summary_op = tf.summary.merge_all() 237 | sess = tf.InteractiveSession(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=False)) 238 | summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph()) 239 | sess.run(init) 240 | 241 | DISPLAY_STEP = 1 242 | index_in_epoch = 0 243 | 244 | for i in range(train_epochs): 245 | # get new batch 246 | batch_xs_path, batch_ys_path, index_in_epoch = _next_batch(train_images, train_labels, batch_size, 247 | index_in_epoch) 248 | batch_xs = np.empty((len(batch_xs_path), self.image_depth, self.image_height, self.image_width, 249 | self.channels)) 250 | batch_ys = np.empty((len(batch_ys_path), self.image_depth, self.image_height, self.image_width, 251 | self.channels)) 252 | for num in range(len(batch_xs_path)): 253 | index = 0 254 | for _ in os.listdir(batch_xs_path[num][0]): 255 | image = cv2.imread(batch_xs_path[num][0] + "/" + str(index) + ".bmp", cv2.IMREAD_GRAYSCALE) 256 | label = cv2.imread(batch_ys_path[num][0] + "/" + str(index) + ".bmp", cv2.IMREAD_GRAYSCALE) 257 | batch_xs[num, index, :, :, :] = np.reshape(image, (self.image_height, self.image_width, 258 | self.channels)) 259 | batch_ys[num, index, :, :, :] = np.reshape(label, (self.image_height, self.image_width, 260 | self.channels)) 261 | index += 1 262 | # Extracting images and labels from given data 263 | batch_xs = batch_xs.astype(np.float) 264 | batch_ys = batch_ys.astype(np.float) 265 | # Normalize from [0:255] => [0.0:1.0] 266 | batch_xs = np.multiply(batch_xs, 1.0 / 255.0) 267 | batch_ys = np.multiply(batch_ys, 1.0 / 255.0) 268 | # check progress on every 1st,2nd,...,10th,20th,...,100th... step 269 | if i % DISPLAY_STEP == 0 or (i + 1) == train_epochs: 270 | train_loss, train_accuracy = sess.run([self.cost, self.accuracy], 271 | feed_dict={self.X: batch_xs, 272 | self.Y_gt: batch_ys, 273 | self.lr: learning_rate, 274 | self.phase: 1, 275 | self.drop: dropout_conv}) 276 | print('epochs %d training_loss ,Training_accuracy => %.5f,%.5f ' % (i, train_loss, train_accuracy)) 277 | 278 | pred = sess.run(self.Y_pred, feed_dict={self.X: batch_xs, 279 | self.Y_gt: batch_ys, 280 | self.phase: 1, 281 | self.drop: 1}) 282 | 283 | gt = np.reshape(batch_xs[0], (64, 128, 128)) 284 | gt = gt.astype(np.float32) 285 | save_images(gt, [8, 8], path='img/Vnet/' + 'src_%d_epoch.png' % (i)) 286 | 287 | gt = np.reshape(batch_ys[0], (64, 128, 128)) 288 | gt = gt.astype(np.float32) 289 | save_images(gt, [8, 8], path='img/Vnet/' + 'gt_%d_epoch.png' % (i)) 290 | 291 | result = np.reshape(pred[0], (64, 128, 128)) 292 | result = result.astype(np.float32) 293 | save_images(result, [8, 8], path='img/Vnet/' + 'predict_%d_epoch.png' % (i)) 294 | 295 | save_path = saver.save(sess, model_path, global_step=self.global_epoch+i+1) 296 | print("Model saved in file:", save_path) 297 | if i % (DISPLAY_STEP * 10) == 0 and i: 298 | DISPLAY_STEP *= 10 299 | 300 | # train on batch 301 | _, summary = sess.run([train_op, merged_summary_op], feed_dict={self.X: batch_xs, 302 | self.Y_gt: batch_ys, 303 | self.lr: learning_rate, 304 | self.phase: 1, 305 | self.drop: dropout_conv}) 306 | summary_writer.add_summary(summary, i) 307 | summary_writer.close() 308 | 309 | 310 | def prediction(self, test_images): 311 | test_images = np.reshape(test_images, (test_images.shape[0], test_images.shape[1], test_images.shape[2], 1)) 312 | test_images = test_images.astype(np.float) 313 | test_images = np.multiply(test_images, 1.0 / 255.0) 314 | y_dummy = test_images 315 | pred = self.sess.run(self.Y_pred, feed_dict={self.X: [test_images], 316 | self.Y_gt: [y_dummy], 317 | self.phase: 1, 318 | self.drop: 1}) 319 | result = pred.astype(np.float32) * 255. 320 | result = np.clip(result, 0, 255).astype('uint8') 321 | result = np.reshape(result, (test_images.shape[0], test_images.shape[1], test_images.shape[2])) 322 | return result 323 | 324 | def restore_training_2(self, model_path): 325 | print("\nReading checkpoints...") 326 | 327 | ckpt = tf.train.get_checkpoint_state(model_path) 328 | if ckpt and ckpt.model_checkpoint_path: 329 | init = tf.global_variables_initializer() 330 | saver = tf.train.Saver() 331 | self.sess = tf.Session() 332 | self.sess.run(init) 333 | 334 | print('Checkpoint file: {}'.format(ckpt.model_checkpoint_path)) 335 | saver.restore(self.sess, ckpt.model_checkpoint_path) 336 | n_epoch = int(ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]) 337 | 338 | print('Loading success, global training epoch is: {}\n'.format(n_epoch)) 339 | return n_epoch 340 | else: 341 | print('No checkpoint file found.\n') 342 | return 343 | -------------------------------------------------------------------------------- /promise2012/Vnet/util.py: -------------------------------------------------------------------------------- 1 | from tensorflow.python.framework import graph_util 2 | from tensorflow.python.framework import graph_io 3 | import tensorflow as tf 4 | 5 | 6 | def convertMetaModelToPbModel(meta_model, pb_model): 7 | # Step 1 8 | # import the model metagraph 9 | saver = tf.train.import_meta_graph(meta_model + '.meta', clear_devices=True) 10 | # make that as the default graph 11 | graph = tf.get_default_graph() 12 | sess = tf.Session() 13 | # now restore the variables 14 | saver.restore(sess, meta_model) 15 | # Step 2 16 | # Find the output name 17 | for op in graph.get_operations(): 18 | print(op.name) 19 | # Step 3 20 | output_graph_def = graph_util.convert_variables_to_constants( 21 | sess, # The session 22 | sess.graph_def, # input_graph_def is useful for retrieving the nodes 23 | ["Placeholder", "output/Sigmoid"]) 24 | 25 | # Step 4 26 | # output folder 27 | output_fld = './' 28 | # output pb file name 29 | output_model_file = 'model.pb' 30 | # write the graph 31 | graph_io.write_graph(output_graph_def, pb_model + output_fld, output_model_file, as_text=False) -------------------------------------------------------------------------------- /promise2012/promise12Vnet3dImage.csv: -------------------------------------------------------------------------------- 1 | filename 2 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_10 3 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_11 4 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_12 5 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_15 6 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_16 7 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_17 8 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_18 9 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_19 10 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_22 11 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_23 12 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_24 13 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_25 14 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_26 15 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_29 16 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_30 17 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_31 18 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_32 19 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_33 20 | D:\Data\PROMISE2012\Vnet3d_data\Vnet3d_patch_train\image/0_8 21 | 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786 | -------------------------------------------------------------------------------- /promise2012/vnet3d_train_predict.py: -------------------------------------------------------------------------------- 1 | from promise2012.Vnet.model_vnet3d import Vnet3dModule 2 | from promise2012.Vnet.util import convertMetaModelToPbModel 3 | import numpy as np 4 | import pandas as pd 5 | import cv2 6 | import tensorflow as tf 7 | tf.logging.set_verbosity(tf.logging.ERROR) 8 | 9 | 10 | def train(): 11 | ''' 12 | Preprocessing for dataset 13 | ''' 14 | # Read data set (Train data from CSV file) 15 | csvmaskdata = pd.read_csv('promise12Vnet3dMask.csv') 16 | csvimagedata = pd.read_csv('promise12Vnet3dImage.csv') 17 | maskdata = csvmaskdata.iloc[:, :].values 18 | imagedata = csvimagedata.iloc[:, :].values 19 | # shuffle imagedata and maskdata together 20 | perm = np.arange(len(csvimagedata)) 21 | np.random.shuffle(perm) 22 | imagedata = imagedata[perm] 23 | maskdata = maskdata[perm] 24 | 25 | Vnet3d = Vnet3dModule(128, 128, 64, channels=1, costname="dice coefficient", model_path="model") 26 | Vnet3d.train(imagedata, maskdata, "model\\my_model", "log\\", 0.001, 0.7, 100000, 1) 27 | 28 | 29 | def predict0(): 30 | Vnet3d = Vnet3dModule(256, 256, 64, inference=True, model_path="model\\Vnet3dModule.pd") 31 | for filenumber in range(30): 32 | batch_xs = np.zeros(shape=(64, 256, 256)) 33 | for index in range(64): 34 | imgs = cv2.imread( 35 | "D:\Data\PROMISE2012\Vnet3d_data\\test\image\\" + str(filenumber) + "\\" + str(index) + ".bmp", 0) 36 | batch_xs[index, :, :] = imgs[128:384, 128:384] 37 | 38 | predictvalue = Vnet3d.prediction(batch_xs) 39 | 40 | for index in range(64): 41 | result = np.zeros(shape=(512, 512), dtype=np.uint8) 42 | result[128:384, 128:384] = predictvalue[index] 43 | kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)) 44 | result = cv2.morphologyEx(result, cv2.MORPH_CLOSE, kernel) 45 | cv2.imwrite( 46 | "D:\Data\PROMISE2012\Vnet3d_data\\test\image\\" + str(filenumber) + "\\" + str(index) + "mask.bmp", 47 | result) 48 | 49 | 50 | def meta2pd(): 51 | convertMetaModelToPbModel(meta_model="model\\Vnet3dModule.pd", pb_model="model") 52 | 53 | train() 54 | #predict0() 55 | #meta2pd() 56 | -------------------------------------------------------------------------------- /src_15_epoch.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/junqiangchen/VNet3D/e8a121a28270b7503eaf1fda85dbf4bd18f9b845/src_15_epoch.png -------------------------------------------------------------------------------- /vnet.PNG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/junqiangchen/VNet3D/e8a121a28270b7503eaf1fda85dbf4bd18f9b845/vnet.PNG --------------------------------------------------------------------------------