├── Images ├── R-UNet.png ├── Results1.png ├── Results2.png └── Results3.png ├── LICENSE.txt ├── README.md ├── Step1_PreProcessing.py ├── Step2_ContourExtraction.py ├── TestFoldAndWeights.zip.001 ├── TestFoldAndWeights.zip.002 ├── TestFoldAndWeights.zip.003 ├── TestFoldAndWeights.zip.004 ├── TestFoldAndWeights.zip.005 ├── TestFoldAndWeights.zip.006 ├── TestFoldAndWeights.zip.007 ├── data_loader.py ├── lib └── FindFiles.py ├── model.py ├── predict.py └── train.py /Images/R-UNet.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huangyjhust/3D-RU-Net/0adaf43b717739a5ddd4b222a2e2ae4381b47f67/Images/R-UNet.png -------------------------------------------------------------------------------- /Images/Results1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huangyjhust/3D-RU-Net/0adaf43b717739a5ddd4b222a2e2ae4381b47f67/Images/Results1.png -------------------------------------------------------------------------------- /Images/Results2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huangyjhust/3D-RU-Net/0adaf43b717739a5ddd4b222a2e2ae4381b47f67/Images/Results2.png -------------------------------------------------------------------------------- /Images/Results3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huangyjhust/3D-RU-Net/0adaf43b717739a5ddd4b222a2e2ae4381b47f67/Images/Results3.png -------------------------------------------------------------------------------- /LICENSE.txt: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. We, the Free Software Foundation, use the 18 | GNU General Public License for most of our software; it applies also to 19 | any other work released this way by its authors. You can apply it to 20 | your programs, too. 21 | 22 | When we speak of free software, we are referring to freedom, not 23 | price. Our General Public Licenses are designed to make sure that you 24 | have the freedom to distribute copies of free software (and charge for 25 | them if you wish), that you receive source code or can get it if you 26 | want it, that you can change the software or use pieces of it in new 27 | free programs, and that you know you can do these things. 28 | 29 | To protect your rights, we need to prevent others from denying you 30 | these rights or asking you to surrender the rights. Therefore, you have 31 | certain responsibilities if you distribute copies of the software, or if 32 | you modify it: responsibilities to respect the freedom of others. 33 | 34 | For example, if you distribute copies of such a program, whether 35 | gratis or for a fee, you must pass on to the recipients the same 36 | freedoms that you received. You must make sure that they, too, receive 37 | or can get the source code. And you must show them these terms so they 38 | know their rights. 39 | 40 | Developers that use the GNU GPL protect your rights with two steps: 41 | (1) assert copyright on the software, and (2) offer you this License 42 | giving you legal permission to copy, distribute and/or modify it. 43 | 44 | For the developers' and authors' protection, the GPL clearly explains 45 | that there is no warranty for this free software. For both users' and 46 | authors' sake, the GPL requires that modified versions be marked as 47 | changed, so that their problems will not be attributed erroneously to 48 | authors of previous versions. 49 | 50 | Some devices are designed to deny users access to install or run 51 | modified versions of the software inside them, although the manufacturer 52 | can do so. This is fundamentally incompatible with the aim of 53 | protecting users' freedom to change the software. The systematic 54 | pattern of such abuse occurs in the area of products for individuals to 55 | use, which is precisely where it is most unacceptable. Therefore, we 56 | have designed this version of the GPL to prohibit the practice for those 57 | products. If such problems arise substantially in other domains, we 58 | stand ready to extend this provision to those domains in future versions 59 | of the GPL, as needed to protect the freedom of users. 60 | 61 | Finally, every program is threatened constantly by software patents. 62 | States should not allow patents to restrict development and use of 63 | software on general-purpose computers, but in those that do, we wish to 64 | avoid the special danger that patents applied to a free program could 65 | make it effectively proprietary. To prevent this, the GPL assures that 66 | patents cannot be used to render the program non-free. 67 | 68 | The precise terms and conditions for copying, distribution and 69 | modification follow. 70 | 71 | TERMS AND CONDITIONS 72 | 73 | 0. Definitions. 74 | 75 | "This License" refers to version 3 of the GNU General Public License. 76 | 77 | "Copyright" also means copyright-like laws that apply to other kinds of 78 | works, such as semiconductor masks. 79 | 80 | "The Program" refers to any copyrightable work licensed under this 81 | License. Each licensee is addressed as "you". "Licensees" and 82 | "recipients" may be individuals or organizations. 83 | 84 | To "modify" a work means to copy from or adapt all or part of the work 85 | in a fashion requiring copyright permission, other than the making of an 86 | exact copy. The resulting work is called a "modified version" of the 87 | earlier work or a work "based on" the earlier work. 88 | 89 | A "covered work" means either the unmodified Program or a work based 90 | on the Program. 91 | 92 | To "propagate" a work means to do anything with it that, without 93 | permission, would make you directly or secondarily liable for 94 | infringement under applicable copyright law, except executing it on a 95 | computer or modifying a private copy. Propagation includes copying, 96 | distribution (with or without modification), making available to the 97 | public, and in some countries other activities as well. 98 | 99 | To "convey" a work means any kind of propagation that enables other 100 | parties to make or receive copies. Mere interaction with a user through 101 | a computer network, with no transfer of a copy, is not conveying. 102 | 103 | An interactive user interface displays "Appropriate Legal Notices" 104 | to the extent that it includes a convenient and prominently visible 105 | feature that (1) displays an appropriate copyright notice, and (2) 106 | tells the user that there is no warranty for the work (except to the 107 | extent that warranties are provided), that licensees may convey the 108 | work under this License, and how to view a copy of this License. If 109 | the interface presents a list of user commands or options, such as a 110 | menu, a prominent item in the list meets this criterion. 111 | 112 | 1. Source Code. 113 | 114 | The "source code" for a work means the preferred form of the work 115 | for making modifications to it. "Object code" means any non-source 116 | form of a work. 117 | 118 | A "Standard Interface" means an interface that either is an official 119 | standard defined by a recognized standards body, or, in the case of 120 | interfaces specified for a particular programming language, one that 121 | is widely used among developers working in that language. 122 | 123 | The "System Libraries" of an executable work include anything, other 124 | than the work as a whole, that (a) is included in the normal form of 125 | packaging a Major Component, but which is not part of that Major 126 | Component, and (b) serves only to enable use of the work with that 127 | Major Component, or to implement a Standard Interface for which an 128 | implementation is available to the public in source code form. A 129 | "Major Component", in this context, means a major essential component 130 | (kernel, window system, and so on) of the specific operating system 131 | (if any) on which the executable work runs, or a compiler used to 132 | produce the work, or an object code interpreter used to run it. 133 | 134 | The "Corresponding Source" for a work in object code form means all 135 | the source code needed to generate, install, and (for an executable 136 | work) run the object code and to modify the work, including scripts to 137 | control those activities. However, it does not include the work's 138 | System Libraries, or general-purpose tools or generally available free 139 | programs which are used unmodified in performing those activities but 140 | which are not part of the work. For example, Corresponding Source 141 | includes interface definition files associated with source files for 142 | the work, and the source code for shared libraries and dynamically 143 | linked subprograms that the work is specifically designed to require, 144 | such as by intimate data communication or control flow between those 145 | subprograms and other parts of the work. 146 | 147 | The Corresponding Source need not include anything that users 148 | can regenerate automatically from other parts of the Corresponding 149 | Source. 150 | 151 | The Corresponding Source for a work in source code form is that 152 | same work. 153 | 154 | 2. Basic Permissions. 155 | 156 | All rights granted under this License are granted for the term of 157 | copyright on the Program, and are irrevocable provided the stated 158 | conditions are met. This License explicitly affirms your unlimited 159 | permission to run the unmodified Program. The output from running a 160 | covered work is covered by this License only if the output, given its 161 | content, constitutes a covered work. This License acknowledges your 162 | rights of fair use or other equivalent, as provided by copyright law. 163 | 164 | You may make, run and propagate covered works that you do not 165 | convey, without conditions so long as your license otherwise remains 166 | in force. You may convey covered works to others for the sole purpose 167 | of having them make modifications exclusively for you, or provide you 168 | with facilities for running those works, provided that you comply with 169 | the terms of this License in conveying all material for which you do 170 | not control copyright. Those thus making or running the covered works 171 | for you must do so exclusively on your behalf, under your direction 172 | and control, on terms that prohibit them from making any copies of 173 | your copyrighted material outside their relationship with you. 174 | 175 | Conveying under any other circumstances is permitted solely under 176 | the conditions stated below. Sublicensing is not allowed; section 10 177 | makes it unnecessary. 178 | 179 | 3. Protecting Users' Legal Rights From Anti-Circumvention Law. 180 | 181 | No covered work shall be deemed part of an effective technological 182 | measure under any applicable law fulfilling obligations under article 183 | 11 of the WIPO copyright treaty adopted on 20 December 1996, or 184 | similar laws prohibiting or restricting circumvention of such 185 | measures. 186 | 187 | When you convey a covered work, you waive any legal power to forbid 188 | circumvention of technological measures to the extent such circumvention 189 | is effected by exercising rights under this License with respect to 190 | the covered work, and you disclaim any intention to limit operation or 191 | modification of the work as a means of enforcing, against the work's 192 | users, your or third parties' legal rights to forbid circumvention of 193 | technological measures. 194 | 195 | 4. Conveying Verbatim Copies. 196 | 197 | You may convey verbatim copies of the Program's source code as you 198 | receive it, in any medium, provided that you conspicuously and 199 | appropriately publish on each copy an appropriate copyright notice; 200 | keep intact all notices stating that this License and any 201 | non-permissive terms added in accord with section 7 apply to the code; 202 | keep intact all notices of the absence of any warranty; and give all 203 | recipients a copy of this License along with the Program. 204 | 205 | You may charge any price or no price for each copy that you convey, 206 | and you may offer support or warranty protection for a fee. 207 | 208 | 5. Conveying Modified Source Versions. 209 | 210 | You may convey a work based on the Program, or the modifications to 211 | produce it from the Program, in the form of source code under the 212 | terms of section 4, provided that you also meet all of these conditions: 213 | 214 | a) The work must carry prominent notices stating that you modified 215 | it, and giving a relevant date. 216 | 217 | b) The work must carry prominent notices stating that it is 218 | released under this License and any conditions added under section 219 | 7. This requirement modifies the requirement in section 4 to 220 | "keep intact all notices". 221 | 222 | c) You must license the entire work, as a whole, under this 223 | License to anyone who comes into possession of a copy. This 224 | License will therefore apply, along with any applicable section 7 225 | additional terms, to the whole of the work, and all its parts, 226 | regardless of how they are packaged. This License gives no 227 | permission to license the work in any other way, but it does not 228 | invalidate such permission if you have separately received it. 229 | 230 | d) If the work has interactive user interfaces, each must display 231 | Appropriate Legal Notices; however, if the Program has interactive 232 | interfaces that do not display Appropriate Legal Notices, your 233 | work need not make them do so. 234 | 235 | A compilation of a covered work with other separate and independent 236 | works, which are not by their nature extensions of the covered work, 237 | and which are not combined with it such as to form a larger program, 238 | in or on a volume of a storage or distribution medium, is called an 239 | "aggregate" if the compilation and its resulting copyright are not 240 | used to limit the access or legal rights of the compilation's users 241 | beyond what the individual works permit. Inclusion of a covered work 242 | in an aggregate does not cause this License to apply to the other 243 | parts of the aggregate. 244 | 245 | 6. Conveying Non-Source Forms. 246 | 247 | You may convey a covered work in object code form under the terms 248 | of sections 4 and 5, provided that you also convey the 249 | machine-readable Corresponding Source under the terms of this License, 250 | in one of these ways: 251 | 252 | a) Convey the object code in, or embodied in, a physical product 253 | (including a physical distribution medium), accompanied by the 254 | Corresponding Source fixed on a durable physical medium 255 | customarily used for software interchange. 256 | 257 | b) Convey the object code in, or embodied in, a physical product 258 | (including a physical distribution medium), accompanied by a 259 | written offer, valid for at least three years and valid for as 260 | long as you offer spare parts or customer support for that product 261 | model, to give anyone who possesses the object code either (1) a 262 | copy of the Corresponding Source for all the software in the 263 | product that is covered by this License, on a durable physical 264 | medium customarily used for software interchange, for a price no 265 | more than your reasonable cost of physically performing this 266 | conveying of source, or (2) access to copy the 267 | Corresponding Source from a network server at no charge. 268 | 269 | c) Convey individual copies of the object code with a copy of the 270 | written offer to provide the Corresponding Source. This 271 | alternative is allowed only occasionally and noncommercially, and 272 | only if you received the object code with such an offer, in accord 273 | with subsection 6b. 274 | 275 | d) Convey the object code by offering access from a designated 276 | place (gratis or for a charge), and offer equivalent access to the 277 | Corresponding Source in the same way through the same place at no 278 | further charge. You need not require recipients to copy the 279 | Corresponding Source along with the object code. If the place to 280 | copy the object code is a network server, the Corresponding Source 281 | may be on a different server (operated by you or a third party) 282 | that supports equivalent copying facilities, provided you maintain 283 | clear directions next to the object code saying where to find the 284 | Corresponding Source. Regardless of what server hosts the 285 | Corresponding Source, you remain obligated to ensure that it is 286 | available for as long as needed to satisfy these requirements. 287 | 288 | e) Convey the object code using peer-to-peer transmission, provided 289 | you inform other peers where the object code and Corresponding 290 | Source of the work are being offered to the general public at no 291 | charge under subsection 6d. 292 | 293 | A separable portion of the object code, whose source code is excluded 294 | from the Corresponding Source as a System Library, need not be 295 | included in conveying the object code work. 296 | 297 | A "User Product" is either (1) a "consumer product", which means any 298 | tangible personal property which is normally used for personal, family, 299 | or household purposes, or (2) anything designed or sold for incorporation 300 | into a dwelling. In determining whether a product is a consumer product, 301 | doubtful cases shall be resolved in favor of coverage. For a particular 302 | product received by a particular user, "normally used" refers to a 303 | typical or common use of that class of product, regardless of the status 304 | of the particular user or of the way in which the particular user 305 | actually uses, or expects or is expected to use, the product. A product 306 | is a consumer product regardless of whether the product has substantial 307 | commercial, industrial or non-consumer uses, unless such uses represent 308 | the only significant mode of use of the product. 309 | 310 | "Installation Information" for a User Product means any methods, 311 | procedures, authorization keys, or other information required to install 312 | and execute modified versions of a covered work in that User Product from 313 | a modified version of its Corresponding Source. The information must 314 | suffice to ensure that the continued functioning of the modified object 315 | code is in no case prevented or interfered with solely because 316 | modification has been made. 317 | 318 | If you convey an object code work under this section in, or with, or 319 | specifically for use in, a User Product, and the conveying occurs as 320 | part of a transaction in which the right of possession and use of the 321 | User Product is transferred to the recipient in perpetuity or for a 322 | fixed term (regardless of how the transaction is characterized), the 323 | Corresponding Source conveyed under this section must be accompanied 324 | by the Installation Information. But this requirement does not apply 325 | if neither you nor any third party retains the ability to install 326 | modified object code on the User Product (for example, the work has 327 | been installed in ROM). 328 | 329 | The requirement to provide Installation Information does not include a 330 | requirement to continue to provide support service, warranty, or updates 331 | for a work that has been modified or installed by the recipient, or for 332 | the User Product in which it has been modified or installed. Access to a 333 | network may be denied when the modification itself materially and 334 | adversely affects the operation of the network or violates the rules and 335 | protocols for communication across the network. 336 | 337 | Corresponding Source conveyed, and Installation Information provided, 338 | in accord with this section must be in a format that is publicly 339 | documented (and with an implementation available to the public in 340 | source code form), and must require no special password or key for 341 | unpacking, reading or copying. 342 | 343 | 7. Additional Terms. 344 | 345 | "Additional permissions" are terms that supplement the terms of this 346 | License by making exceptions from one or more of its conditions. 347 | Additional permissions that are applicable to the entire Program shall 348 | be treated as though they were included in this License, to the extent 349 | that they are valid under applicable law. If additional permissions 350 | apply only to part of the Program, that part may be used separately 351 | under those permissions, but the entire Program remains governed by 352 | this License without regard to the additional permissions. 353 | 354 | When you convey a copy of a covered work, you may at your option 355 | remove any additional permissions from that copy, or from any part of 356 | it. (Additional permissions may be written to require their own 357 | removal in certain cases when you modify the work.) You may place 358 | additional permissions on material, added by you to a covered work, 359 | for which you have or can give appropriate copyright permission. 360 | 361 | Notwithstanding any other provision of this License, for material you 362 | add to a covered work, you may (if authorized by the copyright holders of 363 | that material) supplement the terms of this License with terms: 364 | 365 | a) Disclaiming warranty or limiting liability differently from the 366 | terms of sections 15 and 16 of this License; or 367 | 368 | b) Requiring preservation of specified reasonable legal notices or 369 | author attributions in that material or in the Appropriate Legal 370 | Notices displayed by works containing it; or 371 | 372 | c) Prohibiting misrepresentation of the origin of that material, or 373 | requiring that modified versions of such material be marked in 374 | reasonable ways as different from the original version; or 375 | 376 | d) Limiting the use for publicity purposes of names of licensors or 377 | authors of the material; or 378 | 379 | e) Declining to grant rights under trademark law for use of some 380 | trade names, trademarks, or service marks; or 381 | 382 | f) Requiring indemnification of licensors and authors of that 383 | material by anyone who conveys the material (or modified versions of 384 | it) with contractual assumptions of liability to the recipient, for 385 | any liability that these contractual assumptions directly impose on 386 | those licensors and authors. 387 | 388 | All other non-permissive additional terms are considered "further 389 | restrictions" within the meaning of section 10. If the Program as you 390 | received it, or any part of it, contains a notice stating that it is 391 | governed by this License along with a term that is a further 392 | restriction, you may remove that term. If a license document contains 393 | a further restriction but permits relicensing or conveying under this 394 | License, you may add to a covered work material governed by the terms 395 | of that license document, provided that the further restriction does 396 | not survive such relicensing or conveying. 397 | 398 | If you add terms to a covered work in accord with this section, you 399 | must place, in the relevant source files, a statement of the 400 | additional terms that apply to those files, or a notice indicating 401 | where to find the applicable terms. 402 | 403 | Additional terms, permissive or non-permissive, may be stated in the 404 | form of a separately written license, or stated as exceptions; 405 | the above requirements apply either way. 406 | 407 | 8. Termination. 408 | 409 | You may not propagate or modify a covered work except as expressly 410 | provided under this License. Any attempt otherwise to propagate or 411 | modify it is void, and will automatically terminate your rights under 412 | this License (including any patent licenses granted under the third 413 | paragraph of section 11). 414 | 415 | However, if you cease all violation of this License, then your 416 | license from a particular copyright holder is reinstated (a) 417 | provisionally, unless and until the copyright holder explicitly and 418 | finally terminates your license, and (b) permanently, if the copyright 419 | holder fails to notify you of the violation by some reasonable means 420 | prior to 60 days after the cessation. 421 | 422 | Moreover, your license from a particular copyright holder is 423 | reinstated permanently if the copyright holder notifies you of the 424 | violation by some reasonable means, this is the first time you have 425 | received notice of violation of this License (for any work) from that 426 | copyright holder, and you cure the violation prior to 30 days after 427 | your receipt of the notice. 428 | 429 | Termination of your rights under this section does not terminate the 430 | licenses of parties who have received copies or rights from you under 431 | this License. If your rights have been terminated and not permanently 432 | reinstated, you do not qualify to receive new licenses for the same 433 | material under section 10. 434 | 435 | 9. Acceptance Not Required for Having Copies. 436 | 437 | You are not required to accept this License in order to receive or 438 | run a copy of the Program. Ancillary propagation of a covered work 439 | occurring solely as a consequence of using peer-to-peer transmission 440 | to receive a copy likewise does not require acceptance. However, 441 | nothing other than this License grants you permission to propagate or 442 | modify any covered work. These actions infringe copyright if you do 443 | not accept this License. Therefore, by modifying or propagating a 444 | covered work, you indicate your acceptance of this License to do so. 445 | 446 | 10. Automatic Licensing of Downstream Recipients. 447 | 448 | Each time you convey a covered work, the recipient automatically 449 | receives a license from the original licensors, to run, modify and 450 | propagate that work, subject to this License. You are not responsible 451 | for enforcing compliance by third parties with this License. 452 | 453 | An "entity transaction" is a transaction transferring control of an 454 | organization, or substantially all assets of one, or subdividing an 455 | organization, or merging organizations. If propagation of a covered 456 | work results from an entity transaction, each party to that 457 | transaction who receives a copy of the work also receives whatever 458 | licenses to the work the party's predecessor in interest had or could 459 | give under the previous paragraph, plus a right to possession of the 460 | Corresponding Source of the work from the predecessor in interest, if 461 | the predecessor has it or can get it with reasonable efforts. 462 | 463 | You may not impose any further restrictions on the exercise of the 464 | rights granted or affirmed under this License. For example, you may 465 | not impose a license fee, royalty, or other charge for exercise of 466 | rights granted under this License, and you may not initiate litigation 467 | (including a cross-claim or counterclaim in a lawsuit) alleging that 468 | any patent claim is infringed by making, using, selling, offering for 469 | sale, or importing the Program or any portion of it. 470 | 471 | 11. Patents. 472 | 473 | A "contributor" is a copyright holder who authorizes use under this 474 | License of the Program or a work on which the Program is based. The 475 | work thus licensed is called the contributor's "contributor version". 476 | 477 | A contributor's "essential patent claims" are all patent claims 478 | owned or controlled by the contributor, whether already acquired or 479 | hereafter acquired, that would be infringed by some manner, permitted 480 | by this License, of making, using, or selling its contributor version, 481 | but do not include claims that would be infringed only as a 482 | consequence of further modification of the contributor version. For 483 | purposes of this definition, "control" includes the right to grant 484 | patent sublicenses in a manner consistent with the requirements of 485 | this License. 486 | 487 | Each contributor grants you a non-exclusive, worldwide, royalty-free 488 | patent license under the contributor's essential patent claims, to 489 | make, use, sell, offer for sale, import and otherwise run, modify and 490 | propagate the contents of its contributor version. 491 | 492 | In the following three paragraphs, a "patent license" is any express 493 | agreement or commitment, however denominated, not to enforce a patent 494 | (such as an express permission to practice a patent or covenant not to 495 | sue for patent infringement). To "grant" such a patent license to a 496 | party means to make such an agreement or commitment not to enforce a 497 | patent against the party. 498 | 499 | If you convey a covered work, knowingly relying on a patent license, 500 | and the Corresponding Source of the work is not available for anyone 501 | to copy, free of charge and under the terms of this License, through a 502 | publicly available network server or other readily accessible means, 503 | then you must either (1) cause the Corresponding Source to be so 504 | available, or (2) arrange to deprive yourself of the benefit of the 505 | patent license for this particular work, or (3) arrange, in a manner 506 | consistent with the requirements of this License, to extend the patent 507 | license to downstream recipients. "Knowingly relying" means you have 508 | actual knowledge that, but for the patent license, your conveying the 509 | covered work in a country, or your recipient's use of the covered work 510 | in a country, would infringe one or more identifiable patents in that 511 | country that you have reason to believe are valid. 512 | 513 | If, pursuant to or in connection with a single transaction or 514 | arrangement, you convey, or propagate by procuring conveyance of, a 515 | covered work, and grant a patent license to some of the parties 516 | receiving the covered work authorizing them to use, propagate, modify 517 | or convey a specific copy of the covered work, then the patent license 518 | you grant is automatically extended to all recipients of the covered 519 | work and works based on it. 520 | 521 | A patent license is "discriminatory" if it does not include within 522 | the scope of its coverage, prohibits the exercise of, or is 523 | conditioned on the non-exercise of one or more of the rights that are 524 | specifically granted under this License. You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 3D RU-Net 2 | 3 | Code for the paper entitled "3D RoI-aware U-Net for Accurate and Efficient Colorectal Cancer Segmentation"(https://arxiv.org/abs/1806.10342). 4 | 5 | The latest codes along with weights and a test fold are now released. 6 | 7 | Tips: a recent attempt that transfers training and inferencing to fp16 data format can further enlarge applicable volume sizes. 8 | 9 | ![Fig.0.](https://github.com/huangyjhust/3D-RU-Net/blob/master/Images/R-UNet.png) 10 | 11 | Here are some results of colorectal cancer segmentation, which is the case of the paper; and illustrations of another task, mandible and masseter segmentation, showing the scalability of the proposed method. 12 | 13 | ![Fig.1.](https://github.com/huangyjhust/3D-RU-Net/blob/master/Images/Results1.png) 14 | ![Fig.2.](https://github.com/huangyjhust/3D-RU-Net/blob/master/Images/Results2.png) 15 | 16 | Latest experiment: simultaneously segmenting 14 organs from pelvic CTs in ~0.5s (We trained this model with 24 training samples). 17 | 18 | ![Fig.2.](https://github.com/huangyjhust/3D-RU-Net/blob/master/Images/Results3.png) 19 | -------------------------------------------------------------------------------- /Step1_PreProcessing.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | @author: HuangyjSJTU 4 | """ 5 | import SimpleITK as sitk 6 | import numpy as np 7 | import sys 8 | import os 9 | sys.path.append('./lib/') 10 | import matplotlib.pyplot as pl 11 | from PIL import Image as Img 12 | from FindFiles import findfiles 13 | import dicom 14 | import cv2 15 | from skimage import filters 16 | from skimage.measure import label,regionprops 17 | #For intensity normalization 18 | 19 | DataRoot='../Data/send/' 20 | ModelName='/t2-fov/' 21 | ManualNormalize=True 22 | ResRate=['HighRes','MidRes','LowRes'] 23 | ToSpacing={'HighRes':[1,1,4],'MidRes':[1.5,1.5,4],'LowRes':[2,2,4]} 24 | 25 | def ReadImageAndLabel(CasePath,inverted=False): 26 | #Reading Images 27 | Reader = sitk.ImageSeriesReader() 28 | name=findfiles(CasePath+'img/','*.dcm') 29 | for i in range(len(name)): 30 | name[i]=int(name[i][0:-4]) 31 | name=sorted(name) 32 | name=name[::-1] 33 | for i in range(len(name)): 34 | #print name[i],'\n' 35 | name[i]=CasePath+'img/'+str(name[i])+'.dcm' 36 | 37 | Reader.SetFileNames(name) 38 | Image = Reader.Execute() 39 | Spacing=Image.GetSpacing() 40 | Origin = Image.GetOrigin() 41 | Direction = Image.GetDirection() 42 | 43 | 44 | 45 | #Reading Labels 46 | name=findfiles(CasePath+'label/','*.PNG') 47 | name=sorted(name) 48 | for i in range(len(name)): 49 | name[i]=CasePath+'label/'+name[i] 50 | #print name 51 | #Sometimes labels are inverted along Z axis and should be rectified in this dataset 52 | if inverted: 53 | pass 54 | else: 55 | name=name[::-1] 56 | # for i in range(len(name)): 57 | # print name[i]+'\n' 58 | Reader.SetFileNames(name) 59 | Label = Reader.Execute() 60 | LabelArray=sitk.GetArrayFromImage(Label) 61 | LabelArray=((255-LabelArray[:,:,:,1])).astype(np.uint8)/255 62 | Label=sitk.GetImageFromArray(LabelArray) 63 | Label.SetSpacing(Spacing) 64 | Label.SetOrigin(Origin) 65 | Label.SetDirection(Direction) 66 | return Image,Label 67 | 68 | def Resampling(Image,Label): 69 | Size=Image.GetSize() 70 | Spacing=Image.GetSpacing() 71 | Origin = Image.GetOrigin() 72 | Direction = Image.GetDirection() 73 | ImagePyramid=[] 74 | LabelPyramid=[] 75 | for i in range(3): 76 | NewSpacing = ToSpacing[ResRate[i]] 77 | NewSize=[int(Size[0]*Spacing[0]/NewSpacing[0]),int(Size[1]*Spacing[1]/NewSpacing[1]),int(Size[2]*Spacing[2]/NewSpacing[2])] 78 | Resample = sitk.ResampleImageFilter() 79 | Resample.SetOutputDirection(Direction) 80 | Resample.SetOutputOrigin(Origin) 81 | Resample.SetSize(NewSize) 82 | Resample.SetInterpolator(sitk.sitkLinear) 83 | Resample.SetOutputSpacing(NewSpacing) 84 | NewImage = Resample.Execute(Image) 85 | ImagePyramid.append(NewImage) 86 | 87 | Resample = sitk.ResampleImageFilter() 88 | Resample.SetOutputDirection(Direction) 89 | Resample.SetOutputOrigin(Origin) 90 | Resample.SetSize(NewSize) 91 | Resample.SetOutputSpacing(NewSpacing) 92 | Resample.SetInterpolator(sitk.sitkNearestNeighbor) 93 | NewLabel = Resample.Execute(Label) 94 | LabelPyramid.append(NewLabel) 95 | return ImagePyramid,LabelPyramid 96 | 97 | #We shift the mean value to enhance the darker side 98 | UpperBound=1.0 99 | LowerBound=-4.0 100 | 101 | def Normalization(Image): 102 | Spacing=Image.GetSpacing() 103 | Origin = Image.GetOrigin() 104 | Direction = Image.GetDirection() 105 | Array=sitk.GetArrayFromImage(Image) 106 | Array_new=Array.copy() 107 | Array_new+=np.min(Array_new) 108 | Array_new=Array_new[Array_new.shape[0]/2-5:Array_new.shape[0]/2+5] 109 | Mask=Array_new.copy() 110 | for i in range(Array_new.shape[0]): 111 | otsu=filters.threshold_otsu(Array_new[i]) 112 | Mask[i][Array_new[i]<0.5*otsu]=0 113 | Mask[i][Array_new[i]>=0.5*otsu]=1 114 | MaskSave=sitk.GetImageFromArray(Mask) 115 | MaskSave=sitk.BinaryDilate(MaskSave,10) 116 | MaskSave=sitk.BinaryErode(MaskSave,10) 117 | Mask=sitk.GetArrayFromImage(MaskSave) 118 | 119 | Avg=np.average(Array[Array_new.shape[0]/2-5:Array_new.shape[0]/2+5],weights=Mask) 120 | Std=np.sqrt(np.average(abs(Array[Array_new.shape[0]/2-5:Array_new.shape[0]/2+5] - Avg)**2,weights=Mask)) 121 | Array=(Array.astype(np.float32)-Avg)/Std 122 | Array[Array>UpperBound]=UpperBound 123 | Array[Array0): 38 | LabelErode=Label[z]-cv2.erode(Label[z],kernel) 39 | Contour[z]=LabelErode 40 | Contour=sitk.GetImageFromArray(Contour) 41 | Contour.SetOrigin(Origin) 42 | Contour.SetSpacing(Spacing) 43 | Contour.SetDirection(Direction) 44 | sitk.WriteImage(Contour,Root+Patient+'/'+ResRate+'/'+'Contour.mhd') -------------------------------------------------------------------------------- /TestFoldAndWeights.zip.001: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huangyjhust/3D-RU-Net/0adaf43b717739a5ddd4b222a2e2ae4381b47f67/TestFoldAndWeights.zip.001 -------------------------------------------------------------------------------- /TestFoldAndWeights.zip.002: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huangyjhust/3D-RU-Net/0adaf43b717739a5ddd4b222a2e2ae4381b47f67/TestFoldAndWeights.zip.002 -------------------------------------------------------------------------------- /TestFoldAndWeights.zip.003: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huangyjhust/3D-RU-Net/0adaf43b717739a5ddd4b222a2e2ae4381b47f67/TestFoldAndWeights.zip.003 -------------------------------------------------------------------------------- /TestFoldAndWeights.zip.004: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huangyjhust/3D-RU-Net/0adaf43b717739a5ddd4b222a2e2ae4381b47f67/TestFoldAndWeights.zip.004 -------------------------------------------------------------------------------- /TestFoldAndWeights.zip.005: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huangyjhust/3D-RU-Net/0adaf43b717739a5ddd4b222a2e2ae4381b47f67/TestFoldAndWeights.zip.005 -------------------------------------------------------------------------------- /TestFoldAndWeights.zip.006: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huangyjhust/3D-RU-Net/0adaf43b717739a5ddd4b222a2e2ae4381b47f67/TestFoldAndWeights.zip.006 -------------------------------------------------------------------------------- /TestFoldAndWeights.zip.007: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/huangyjhust/3D-RU-Net/0adaf43b717739a5ddd4b222a2e2ae4381b47f67/TestFoldAndWeights.zip.007 -------------------------------------------------------------------------------- /data_loader.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Sat Feb 16 21:27:54 2019 5 | 6 | @author: customer 7 | """ 8 | import numpy as np 9 | import random 10 | import SimpleITK as sitk 11 | from skimage.measure import label,regionprops 12 | from skimage import filters 13 | import torch 14 | #Maximum Bbox Cropping to Reduce Image Dimension 15 | def MaxBodyBox(input): 16 | Otsu=filters.threshold_otsu(input[input.shape[0]//2]) 17 | Seg=np.zeros(input.shape) 18 | Seg[input>=Otsu]=255 19 | Seg=Seg.astype(np.int) 20 | ConnectMap=label(Seg, connectivity= 2) 21 | Props = regionprops(ConnectMap) 22 | Area=np.zeros([len(Props)]) 23 | Area=[] 24 | Bbox=[] 25 | for j in range(len(Props)): 26 | Area.append(Props[j]['area']) 27 | Bbox.append(Props[j]['bbox']) 28 | Area=np.array(Area) 29 | Bbox=np.array(Bbox) 30 | argsort=np.argsort(Area) 31 | Area=Area[argsort] 32 | Bbox=Bbox[argsort] 33 | Area=Area[::-1] 34 | Bbox=Bbox[::-1,:] 35 | MaximumBbox=Bbox[0] 36 | return Otsu,MaximumBbox 37 | 38 | def DataLoader(Patient,opt,Subset='Train'): 39 | assert Subset in ['Train','Valid','Test'] 40 | #Image Loading 41 | ImageInput=sitk.ReadImage(opt.DATA_ROOT+'/'+Subset+'/'+Patient+'/HighRes/'+'Image_2.mhd') 42 | ImageInput=sitk.GetArrayFromImage(ImageInput) 43 | RegionLabel=sitk.ReadImage(opt.DATA_ROOT+'/'+Subset+'/'+Patient+'/HighRes/'+'Label.mhd') 44 | RegionLabel=sitk.GetArrayFromImage(RegionLabel) 45 | ContourLabel=sitk.ReadImage(opt.DATA_ROOT+'/'+Subset+'/'+Patient+'/HighRes/'+'Contour.mhd') 46 | ContourLabel=sitk.GetArrayFromImage(ContourLabel) 47 | #Orig Shape Backup 48 | Shape=ImageInput.shape 49 | #Body Bbox Compute 50 | Otsu,MaximumBbox=MaxBodyBox(ImageInput) 51 | 52 | 53 | #Apply BodyBbox Cropping 54 | ImageInput=ImageInput[MaximumBbox[0]:MaximumBbox[3],MaximumBbox[1]:MaximumBbox[4],MaximumBbox[2]:MaximumBbox[5]] 55 | RegionLabel=RegionLabel[MaximumBbox[0]:MaximumBbox[3],MaximumBbox[1]:MaximumBbox[4],MaximumBbox[2]:MaximumBbox[5]] 56 | ContourLabel=ContourLabel[MaximumBbox[0]:MaximumBbox[3],MaximumBbox[1]:MaximumBbox[4],MaximumBbox[2]:MaximumBbox[5]] 57 | 58 | if Subset=='Train': 59 | Xinvert=random.randint(0,1) 60 | IntensityScale=random.uniform(0.9,1.1) 61 | else: 62 | Xinvert=False 63 | IntensityScale=1 64 | #Apply Intensity Jitterring 65 | ImageInput=((ImageInput-128.0)*IntensityScale+128.0)/255 66 | ImageInput[ImageInput>1]=1 67 | ImageInput[ImageInput<0]=0 68 | #Apply Random Flipping 69 | if Xinvert: 70 | ImageInput=ImageInput[:,:,::-1].copy() 71 | RegionLabel=RegionLabel[:,:,::-1].copy() 72 | ContourLabel=ContourLabel[:,:,::-1].copy() 73 | 74 | #To Tensor 75 | ImageTensor=np.zeros([1,1,ImageInput.shape[0],ImageInput.shape[1],ImageInput.shape[2]]) 76 | ImageTensor[0,0]=ImageInput 77 | ImageTensor=ImageTensor.astype(np.float) 78 | ImageTensor=torch.from_numpy(ImageTensor) 79 | ImageTensor=ImageTensor.float() 80 | ImageTensor = ImageTensor.to(device=opt.GPU) 81 | 82 | RegionLabelTensor=np.zeros([1,2,ImageInput.shape[0],ImageInput.shape[1],ImageInput.shape[2]]) 83 | RegionLabelTensor[0,1]=RegionLabel 84 | RegionLabelTensor[0,0]=1-RegionLabel 85 | RegionLabelTensor=torch.from_numpy(RegionLabelTensor) 86 | RegionLabelTensor=RegionLabelTensor.float() 87 | RegionLabelTensor=RegionLabelTensor.to(device=opt.GPU) 88 | 89 | ContourLabelTensor=np.zeros([1,2,ImageInput.shape[0],ImageInput.shape[1],ImageInput.shape[2]]) 90 | ContourLabelTensor[0,1]=ContourLabel 91 | ContourLabelTensor[0,0]=1-ContourLabel 92 | ContourLabelTensor=torch.from_numpy(ContourLabelTensor) 93 | ContourLabelTensor=ContourLabelTensor.float() 94 | ContourLabelTensor=ContourLabelTensor.to(device=opt.GPU) 95 | 96 | 97 | return ImageTensor,RegionLabelTensor,ContourLabelTensor,Shape,MaximumBbox 98 | 99 | def ArbitraryDataLoader(Patient,opt,Subset='Test'): 100 | #Image Loading 101 | ImageInput=sitk.ReadImage(opt.DATA_ROOT+'/'+Subset+'/'+Patient+'/HighRes/'+'Image_2.mhd') 102 | ImageInput=sitk.GetArrayFromImage(ImageInput)/255.0 103 | #Orig Shape Backup 104 | Shape=ImageInput.shape 105 | #Body Bbox Compute 106 | Otsu,MaximumBbox=MaxBodyBox(ImageInput) 107 | 108 | 109 | #Apply BodyBbox Cropping 110 | ImageInput=ImageInput[MaximumBbox[0]:MaximumBbox[3],MaximumBbox[1]:MaximumBbox[4],MaximumBbox[2]:MaximumBbox[5]] 111 | 112 | #To Tensor 113 | ImageTensor=np.zeros([1,1,ImageInput.shape[0],ImageInput.shape[1],ImageInput.shape[2]]) 114 | ImageTensor[0,0]=ImageInput 115 | ImageTensor=ImageTensor.astype(np.float) 116 | ImageTensor=torch.from_numpy(ImageTensor) 117 | ImageTensor=ImageTensor.float() 118 | ImageTensor = ImageTensor.to(device=opt.GPU) 119 | 120 | 121 | 122 | 123 | return ImageTensor,Shape,MaximumBbox -------------------------------------------------------------------------------- /lib/FindFiles.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Tue Aug 08 09:56:29 2017 4 | 5 | @author: Administrator 6 | """ 7 | import os 8 | import glob 9 | 10 | def findfiles(dirname,pattern): 11 | cwd = os.getcwd() #保存当前工作目录 12 | if dirname: 13 | os.chdir(dirname) 14 | 15 | result = [] 16 | for filename in glob.iglob(pattern): #此处可以用glob.glob(pattern) 返回所有结果 17 | result.append(filename) 18 | #恢复工作目录 19 | os.chdir(cwd) 20 | return result -------------------------------------------------------------------------------- /model.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Sat Feb 16 20:58:07 2019 5 | 6 | @author: customer 7 | """ 8 | 9 | import numpy as np 10 | import torch 11 | import torch.nn as nn 12 | import torch.nn.functional as F 13 | import time 14 | from skimage.measure import label,regionprops 15 | 16 | class ResBlock(nn.Module): 17 | '''(conv => BN => ReLU) * 2''' 18 | def __init__(self, in_ch, out_ch, kernel, Inplace=True,Dilation=1): 19 | super(ResBlock, self).__init__() 20 | padding=((kernel[0]-1)//2,Dilation,Dilation) 21 | dilation=(1,Dilation,Dilation) 22 | self.Conv1=nn.Conv3d(in_ch, out_ch, kernel, padding=padding,dilation=dilation) 23 | self.BN1=torch.nn.InstanceNorm3d(out_ch) 24 | self.Relu=nn.ReLU(inplace=Inplace) 25 | self.Conv2=nn.Conv3d(out_ch, out_ch, kernel, padding=padding,dilation=dilation) 26 | self.BN2=torch.nn.InstanceNorm3d(out_ch) 27 | self.Conv3=nn.Conv3d(out_ch, out_ch, kernel, padding=padding,dilation=dilation) 28 | self.BN3=torch.nn.InstanceNorm3d(out_ch) 29 | def forward(self, x): 30 | x1 = self.Conv1(x) 31 | x2 = self.BN1(x1) 32 | x3 = self.Relu(x2) 33 | x4 = self.Conv2(x3) 34 | x5 = self.BN2(x4) 35 | x6 = self.Relu(x5) 36 | x7 = self.Conv3(x6) 37 | x8 = self.BN3(x7) 38 | x9 = torch.add(x8,x1) 39 | x10 = self.Relu(x9) 40 | return x10 41 | 42 | 43 | class inconv(nn.Module): 44 | def __init__(self, in_ch, out_ch, Inplace, Dilation=1): 45 | super(inconv, self).__init__() 46 | self.conv = ResBlock(in_ch, out_ch, (1,3,3), Inplace,Dilation) 47 | def forward(self, x): 48 | x = self.conv(x) 49 | return x 50 | 51 | 52 | class down(nn.Module): 53 | def __init__(self, in_ch, out_ch, p_kernel, Inplace,Dilation=1): 54 | super(down, self).__init__() 55 | self.mpconv = nn.Sequential( 56 | nn.MaxPool3d(p_kernel), 57 | ResBlock(in_ch, out_ch, (3,3,3), Inplace,Dilation) 58 | ) 59 | 60 | def forward(self, x): 61 | x = self.mpconv(x) 62 | return x 63 | 64 | 65 | class up(nn.Module): 66 | def __init__(self, in_ch, out_ch, p_kernel, c_kernel, Inplace=True,learn=False,Dilation=1): 67 | super(up, self).__init__() 68 | self.p_kernel=p_kernel 69 | self.learn=learn 70 | if self.learn: 71 | self.up = nn.ConvTranspose3d(in_ch, out_ch, 2, stride=2)#torch.upsample(in_ch, out_ch,)#nn.ConvTranspose3d(in_ch, out_ch, 2, stride=2) 72 | self.fuse = ResBlock(in_ch, out_ch, c_kernel, Inplace,Dilation) 73 | self.conv = nn.Conv3d(in_ch, out_ch, (1,1,1)) 74 | self.Relu=nn.ReLU(inplace=Inplace) 75 | def forward(self, x1, x2): 76 | if not self.learn: 77 | x1 = F.upsample(x1, size=(x1.size()[2]*self.p_kernel[0],x1.size()[3]*self.p_kernel[1],x1.size()[4]*self.p_kernel[2]),mode='trilinear') 78 | x1 = self.conv(x1) 79 | x1 = self.Relu(x1) 80 | x = torch.cat([x2, x1], dim=1) 81 | x = self.fuse(x) 82 | return x 83 | 84 | class OutconvG(nn.Module): 85 | def __init__(self, in_ch, out_ch): 86 | super(OutconvG, self).__init__() 87 | self.conv = nn.Conv3d(in_ch, out_ch, 1) 88 | 89 | def forward(self, x): 90 | x = self.conv(x) 91 | return x 92 | class OutconvR(nn.Module): 93 | def __init__(self, in_ch, out_ch): 94 | super(OutconvR, self).__init__() 95 | self.conv = nn.Conv3d(in_ch, out_ch, 1) 96 | 97 | def forward(self, x): 98 | x = self.conv(x) 99 | return x 100 | 101 | class OutconvC(nn.Module): 102 | def __init__(self, in_ch, out_ch): 103 | super(OutconvC, self).__init__() 104 | self.conv = nn.Conv3d(in_ch, out_ch, 1) 105 | 106 | def forward(self, x): 107 | x = self.conv(x) 108 | return x 109 | 110 | class GlobalImageEncoder(nn.Module): 111 | def __init__(self, opt): 112 | super(GlobalImageEncoder, self).__init__() 113 | self.opt=opt 114 | self.n_classes=len(opt.DICT_CLASS.keys()) 115 | self.Inplace=True 116 | self.Base=opt.BASE_CHANNELS 117 | self.inc = inconv(1, self.Base,self.Inplace,Dilation=opt.STAGE_DILATION[0]) 118 | self.down1 = down(self.Base, self.Base*2,(1,2,2),self.Inplace,Dilation=opt.STAGE_DILATION[1]) 119 | self.down2 = down(self.Base*2, self.Base*4, (2,2,2),self.Inplace,Dilation=opt.STAGE_DILATION[2]) 120 | self.LocTop = OutconvG(self.Base*4, self.n_classes) 121 | def forward(self,x): 122 | x1 = self.inc(x) 123 | x2 = self.down1(x1) 124 | x3 = self.down2(x2) 125 | LocOut=self.LocTop(x3) 126 | LocOut=F.softmax(LocOut) 127 | return LocOut,[x1,x2,x3] 128 | def TrainForward(self,x,y,GetGlobalFeat=False): 129 | y= F.max_pool3d(y,kernel_size=(2,4,4),stride=(2,4,4)) 130 | LocOut,GlobalFeatPyramid=self.forward(x) 131 | if GetGlobalFeat: 132 | return LocOut,y,GlobalFeatPyramid 133 | else: 134 | return LocOut,y 135 | class LocalRegionDecoder(nn.Module): 136 | def __init__(self, opt): 137 | super(LocalRegionDecoder, self).__init__() 138 | self.opt=opt 139 | self.n_classes=len(opt.DICT_CLASS.keys()) 140 | self.Inplace=True 141 | self.Base=opt.BASE_CHANNELS 142 | self.up1 = up(self.Base*4, self.Base*2,(2,2,2),(3,3,3),self.Inplace,False,Dilation=opt.STAGE_DILATION[1]) 143 | self.up2 = up(self.Base*2, self.Base,(1,2,2),(1,3,3),self.Inplace,False,Dilation=opt.STAGE_DILATION[0]) 144 | self.SegTop1 = OutconvR(self.Base, self.n_classes) 145 | self.SegTop2 = OutconvC(self.Base, self.n_classes) 146 | def forward(self,GlobalFeatPyramid,RoIs): 147 | x1=GlobalFeatPyramid[0] 148 | x2=GlobalFeatPyramid[1] 149 | x3=GlobalFeatPyramid[2] 150 | P_Region=[] 151 | P_Contour=[] 152 | 153 | for i in range(len(RoIs)): 154 | Zstart=RoIs[i][0] 155 | Ystart=RoIs[i][1] 156 | Xstart=RoIs[i][2] 157 | Zend=RoIs[i][3] 158 | Yend=RoIs[i][4] 159 | Xend=RoIs[i][5] 160 | #RoI TensorPyramid 161 | RoiTensorPyramid=[x3[:,:,Zstart:Zend,Ystart:Yend,Xstart:Xend],\ 162 | x2[:,:,Zstart*2:Zend*2,Ystart*2:Yend*2,Xstart*2:Xend*2],\ 163 | x1[:,:,Zstart*2:Zend*2,Ystart*4:Yend*4,Xstart*4:Xend*4]] 164 | 165 | p = self.up1(RoiTensorPyramid[0], RoiTensorPyramid[1]) 166 | p = self.up2(p, RoiTensorPyramid[2]) 167 | p_r = self.SegTop1(p) 168 | p_r = F.softmax(p_r) 169 | 170 | p_c = self.SegTop2(p) 171 | p_c = F.softmax(p_c) 172 | 173 | P_Region.append(p_r) 174 | P_Contour.append(p_c) 175 | return P_Region,P_Contour 176 | def TrainForward(self,GlobalFeatPyramid,RoIs,y_region,y_contour): 177 | Y_Region=[] 178 | Y_Contour=[] 179 | #Extract in-region labels 180 | for i in range(len(RoIs)): 181 | Zstart=RoIs[i][0] 182 | Ystart=RoIs[i][1] 183 | Xstart=RoIs[i][2] 184 | Zend=RoIs[i][3] 185 | Yend=RoIs[i][4] 186 | Xend=RoIs[i][5] 187 | y_region_RoI=y_region[:,:,Zstart*2:Zend*2,Ystart*4:Yend*4,Xstart*4:Xend*4] 188 | y_contour_RoI=y_contour[:,:,Zstart*2:Zend*2,Ystart*4:Yend*4,Xstart*4:Xend*4] 189 | Y_Region.append(y_region_RoI) 190 | Y_Contour.append(y_contour_RoI) 191 | P_Region,P_Contour=self.forward(GlobalFeatPyramid,RoIs) 192 | return P_Region,P_Contour,Y_Region,Y_Contour 193 | 194 | class RU_Net(nn.Module): 195 | def __init__(self, opt): 196 | super(RU_Net, self).__init__() 197 | self.opt=opt 198 | self.n_classes=len(opt.DICT_CLASS.keys()) 199 | self.Inplace=True 200 | self.Base=48 201 | self.GlobalImageEncoder=GlobalImageEncoder(opt) 202 | self.LocalRegionDecoder=LocalRegionDecoder(opt) 203 | def forward_RoI_Loc(self, x,y): 204 | LocOut,Y=self.GlobalImageEncoder.TrainForward(x,y,False) 205 | return [LocOut,Y] 206 | def Localization(self,LocOut,Train=True): 207 | if Train: 208 | MAX_ROIS=self.opt.MAX_ROIS_TRAIN 209 | else: 210 | MAX_ROIS=self.opt.MAX_ROIS_TEST 211 | LocOut = LocOut.to(device='cpu') 212 | LocOut = LocOut.detach().numpy() 213 | RoIs=[] 214 | #num=0 215 | for i in range(1,self.n_classes): 216 | Heatmap = LocOut[0,i] 217 | Heatmap = (Heatmap-np.min(Heatmap))/(np.max(Heatmap)-np.min(Heatmap)) 218 | Heatmap[Heatmap<0.5]=0 219 | Heatmap[Heatmap>=0.5]=1 220 | Heatmap*=255 221 | ConnectMap=label(Heatmap, connectivity= 2) 222 | Props = regionprops(ConnectMap) 223 | Area=np.zeros([len(Props)]) 224 | Area=[] 225 | Bbox=[] 226 | for j in range(len(Props)): 227 | Area.append(Props[j]['area']) 228 | Bbox.append(list(Props[j]['bbox'])) 229 | OverDesignRange=[1,2,2] 230 | for k in range(3): 231 | if Bbox[j][k]-OverDesignRange[k]<0: 232 | Bbox[j][k]=0 233 | else: 234 | Bbox[j][k]-=OverDesignRange[k] 235 | for k in range(3,6): 236 | if Bbox[j][k]+OverDesignRange[k-3]>=Heatmap.shape[k-3]-1: 237 | Bbox[j][k]=Heatmap.shape[k-3]-1 238 | else: 239 | Bbox[j][k]+=OverDesignRange[k-3] 240 | Area=np.array(Area) 241 | Bbox=np.array(Bbox) 242 | argsort=np.argsort(Area) 243 | Area=Area[argsort] 244 | Bbox=Bbox[argsort] 245 | Area=Area[::-1] 246 | Bbox=Bbox[::-1,:] 247 | 248 | max_boxes=MAX_ROIS[self.opt.DICT_CLASS[i]] 249 | if Area.shape[0]>=max_boxes: 250 | OutBbox=Bbox[:max_boxes,:] 251 | elif Area.shape[0]==0: 252 | OutBbox=np.zeros([1,6],dtype=np.int) 253 | OutBbox[0]=[0,0,0,1,1,1] 254 | else: 255 | OutBbox=Bbox 256 | for j in range(OutBbox.shape[0]): 257 | RoIs.append(OutBbox[j,:]) 258 | 259 | return RoIs 260 | 261 | 262 | def TrainForward(self, x, y_region, y_contour): 263 | LocOut,y_region_down,GlobalFeatPyramid=self.GlobalImageEncoder.TrainForward(x,y_region,True) 264 | RoIs=self.Localization(LocOut,Train=True) 265 | P_Region,P_Contour,Y_Region,Y_Contour=self.LocalRegionDecoder.TrainForward(GlobalFeatPyramid,RoIs,y_region,y_contour) 266 | 267 | 268 | return P_Region,P_Contour,Y_Region,Y_Contour,RoIs,[LocOut,y_region_down] 269 | def forward(self, x): 270 | LocOut,GlobalFeatPyramid=self.GlobalImageEncoder.forward(x) 271 | RoIs=self.Localization(LocOut,Train=False) 272 | P_Region,P_Contour=self.LocalRegionDecoder(GlobalFeatPyramid,RoIs) 273 | return P_Region,P_Contour,RoIs -------------------------------------------------------------------------------- /predict.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python2 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Thu Jun 28 13:03:37 2018 5 | 6 | @author: customer 7 | """ 8 | 9 | import numpy as np 10 | from model import RU_Net 11 | from train import Config 12 | from data_loader import ArbitraryDataLoader 13 | 14 | import os 15 | import SimpleITK as sitk 16 | import numpy as np 17 | import random 18 | import torch 19 | import torch.nn as nn 20 | from torch import optim 21 | import torch.nn.functional as F 22 | from torch.autograd import Variable 23 | import time 24 | from graphviz import Digraph 25 | from skimage.measure import label,regionprops 26 | from matplotlib import pyplot as pl 27 | from skimage import filters 28 | from skimage import data,util,transform 29 | 30 | 31 | 32 | def Predict(Model,ImageTensor,Shape,MaximumBbox): 33 | with torch.no_grad(): 34 | PredSeg=Model.forward(ImageTensor) 35 | RegionOutput=np.zeros(ImageTensor.shape) 36 | RegionWeight=np.zeros(ImageTensor.shape)+0.001 37 | RoIs=PredSeg[2] 38 | #apply RoI predictions to a body-cropped large volume container 39 | #average predictions if RoIs are overlapped 40 | for i in range(len(PredSeg[0])): 41 | Coord=RoIs[i]*np.array([2,4,4,2,4,4]) 42 | Weight=np.ones(np.asarray(PredSeg[0][i][0].shape)) 43 | RegionOutput[0,:,Coord[0]:Coord[3],Coord[1]:Coord[4],Coord[2]:Coord[5]]+=PredSeg[0][i][0,1:].to('cpu').detach().numpy() 44 | RegionWeight[0,:,Coord[0]:Coord[3],Coord[1]:Coord[4],Coord[2]:Coord[5]]+=Weight[1:] 45 | RegionOutput/=RegionWeight 46 | 47 | 48 | 49 | return RegionOutput 50 | 51 | if __name__=='__main__': 52 | 53 | opt=[Config('RF64'),Config('RF88'),Config('RF112')] 54 | Models=[RU_Net(opt[0]).to(opt[0].GPU),RU_Net(opt[1]).to(opt[1].GPU),RU_Net(opt[2]).to(opt[2].GPU)] 55 | for mid,Model in enumerate(Models): 56 | Model.load_state_dict(torch.load(opt[mid].WEIGHT_PATH)) 57 | Model.eval() 58 | Root='./Data/Test/' 59 | PatientNames=os.listdir(Root) 60 | PatientNames=sorted(PatientNames) 61 | NumPatients=len(PatientNames) 62 | for i in range(NumPatients): 63 | Patient=PatientNames[i] 64 | ImageTensor,Shape,MaximumBbox=ArbitraryDataLoader(Patient,opt[0],'Test') 65 | RegionOutput=np.zeros(ImageTensor.shape) 66 | 67 | time1=time.time() 68 | #Ensemble by averaging predictions 69 | for j in range(len(Models)): 70 | RegionOutput+=Predict(Models[j],ImageTensor,Shape,MaximumBbox) 71 | RegionOutput/=len(Models) 72 | print("time used: ",time.time()-time1) 73 | 74 | #body-cropped volume back to whole volume container 75 | OutputWhole=np.zeros(Shape,dtype=np.float) 76 | OutputWhole[MaximumBbox[0]:MaximumBbox[3],MaximumBbox[1]:MaximumBbox[4],MaximumBbox[2]:MaximumBbox[5]]=RegionOutput[0,0] 77 | #Back to ITK images for storage 78 | OutputWhole*=255 79 | OutputWhole=OutputWhole.astype(np.uint8) 80 | OutputWhole=sitk.GetImageFromArray(OutputWhole) 81 | OutputWhole.SetSpacing(opt[0].TO_SPACING) 82 | sitk.WriteImage(OutputWhole,'./Output/'+Patient+'/EnsemblePreds.mhd') 83 | 84 | 85 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python2 2 | # -*- coding: utf-8 -*- 3 | """ 4 | Created on Mon Jun 11 15:10:18 2018 5 | 6 | @author: customer 7 | """ 8 | import os 9 | import SimpleITK as sitk 10 | import numpy as np 11 | import random 12 | import torch 13 | from torch import optim 14 | import torch.nn.functional as F 15 | from torch.autograd import Variable 16 | import cv2 17 | 18 | from data_loader import DataLoader 19 | from model import RU_Net 20 | inplace=True 21 | 22 | 23 | STAGE_DILATIONS={'RF64':[1,1,1],'RF88':[1,1,2],'RF112':[1,2,2]} 24 | TAG='RF112'# or 'RF64' or 'RF88' 25 | class Config(): 26 | def __init__(self,TAG): 27 | self.TAG=TAG 28 | self.STAGE_DILATION=STAGE_DILATIONS[TAG] 29 | self.DICT_CLASS={0:'Background', 1:'Cancer'} 30 | self.MAX_ROIS_TEST={'Background':0,'Cancer':10} 31 | self.MAX_ROIS_TRAIN={'Background':0,'Cancer':2} 32 | self.MAX_ROI_SIZE=[24,96,96] 33 | self.TO_SPACING=[1,1,4] 34 | self.DOWN_SAMPLE=[2,4,4] 35 | self.DATA_ROOT='./Data/' 36 | self.INPLACE=True 37 | self.GPU='cuda:1' 38 | self.MAX_EPOCHS=50 39 | self.WEIGHT_PATH='./Weights/'+self.TAG+'.pkl' 40 | self.TEST_ONLY=False 41 | self.BASE_CHANNELS=48 42 | opt=Config(TAG) 43 | 44 | 45 | def MultiClassDiceLossFunc(y_pred,y_true): 46 | overlap=torch.zeros([1]).cuda(opt.GPU) 47 | bottom=torch.zeros([1]).cuda(opt.GPU) 48 | for i in range(1,len(opt.DICT_CLASS.keys())): 49 | overlap+=torch.sum(y_pred[0,i]*y_true[0,i]) 50 | bottom+=torch.sum(y_pred[0,i])+torch.sum(y_true[0,i]) 51 | return 1-2*(overlap+1e-4)/(bottom+1e-4) 52 | def RoIDiceLossFunc(y_pred,y_true): 53 | overlap=torch.zeros([1]).cuda(opt.GPU) 54 | bottom=torch.zeros([1]).cuda(opt.GPU) 55 | for i in range(len(y_pred)): 56 | for j in range(1,len(opt.DICT_CLASS.keys())): 57 | overlap+=torch.sum(y_pred[i][0,j]*y_true[i][0,j]) 58 | bottom+=torch.sum(y_pred[i][0,j])+torch.sum(y_true[i][0,j]) 59 | return (1-2*overlap/bottom) 60 | 61 | 62 | def Predict(Patient,Subset): 63 | Image,LabelRegion,LabelContour,Shape,MaximumBbox=DataLoader(Patient,opt,Subset) 64 | Label=LabelRegion.to('cpu').detach().numpy() 65 | 66 | with torch.no_grad(): 67 | PredSeg=Model.forward(Image) 68 | RegionOutput=np.zeros(Label.shape) 69 | RegionWeight=np.zeros(Label.shape)+0.001 70 | RoIs=PredSeg[2] 71 | #Apply RoI region predictions to in-body volume container 72 | #If overlapped, average 73 | for i in range(len(PredSeg[0])): 74 | Coord=RoIs[i]*np.array([2,4,4,2,4,4]) 75 | Weight=np.ones(np.asarray(PredSeg[0][i][0].shape)) 76 | RegionOutput[0,:,Coord[0]:Coord[3],Coord[1]:Coord[4],Coord[2]:Coord[5]]+=PredSeg[0][i][0]#.to('cpu').detach().numpy() 77 | RegionWeight[0,:,Coord[0]:Coord[3],Coord[1]:Coord[4],Coord[2]:Coord[5]]+=Weight 78 | RegionOutput/=RegionWeight 79 | 80 | #Apply RoI contour predictions to in-body volume container 81 | #If overlapped, average 82 | ContourOutput=np.zeros(Label.shape) 83 | ContourWeight=np.zeros(Label.shape)+0.001 84 | RoIs=PredSeg[2] 85 | for i in range(len(PredSeg[0])): 86 | Coord=RoIs[i]*np.array([2,4,4,2,4,4]) 87 | Weight=np.ones(np.asarray(PredSeg[0][i][0].shape)) 88 | ContourOutput[0,:,Coord[0]:Coord[3],Coord[1]:Coord[4],Coord[2]:Coord[5]]+=PredSeg[1][i][0]#.to('cpu').detach().numpy() 89 | ContourWeight[0,:,Coord[0]:Coord[3],Coord[1]:Coord[4],Coord[2]:Coord[5]]+=Weight 90 | ContourOutput/=ContourWeight 91 | 92 | #Apply in-body volume container to original volume size 93 | OutputWhole1=np.zeros(Shape,dtype=np.uint8) 94 | OutputWhole2=np.zeros(Shape,dtype=np.uint8) 95 | OutputWhole=np.zeros(Shape,dtype=np.uint8) 96 | OutputWhole1[MaximumBbox[0]:MaximumBbox[3],MaximumBbox[1]:MaximumBbox[4],MaximumBbox[2]:MaximumBbox[5]]=(RegionOutput[0,1]*255).astype(np.uint8) 97 | OutputWhole2[MaximumBbox[0]:MaximumBbox[3],MaximumBbox[1]:MaximumBbox[4],MaximumBbox[2]:MaximumBbox[5]]=(ContourOutput[0,1]*255).astype(np.uint8) 98 | #Save binary predictions 99 | OutputWhole[OutputWhole1>=128]=1 100 | OutputWhole[OutputWhole1<128]=0 101 | RegionOutput[RegionOutput>=0.5]=1 102 | RegionOutput[RegionOutput<0.5]=0 103 | Loss=1-2*np.sum(RegionOutput[0,1]*Label[0,1])/(np.sum(RegionOutput[0,1])+np.sum(Label[0,1])) 104 | OutputWhole1=sitk.GetImageFromArray(OutputWhole1) 105 | OutputWhole1.SetSpacing(opt.TO_SPACING) 106 | OutputWhole2=sitk.GetImageFromArray(OutputWhole2) 107 | OutputWhole2.SetSpacing(opt.TO_SPACING) 108 | 109 | #Draw bounding-boxes 110 | for Rid in range(len(RoIs)): 111 | color=(Rid+1,Rid+1,Rid+1) 112 | 113 | Coord=RoIs[Rid]*np.array([2,4,4,2,4,4])+np.array([MaximumBbox[0],MaximumBbox[1],MaximumBbox[2],MaximumBbox[0],MaximumBbox[1],MaximumBbox[2]]) 114 | #Out-of-volume protection 115 | for protect in range(3): 116 | if Coord[protect+3]>=OutputWhole.shape[protect+0]: 117 | Coord[protect+3]=OutputWhole.shape[protect+0]-1 118 | #Draw rectangles 119 | Rgb=np.zeros([OutputWhole.shape[1],OutputWhole.shape[2],3],dtype=np.uint8) 120 | Rgb[:,:,0]=OutputWhole[Coord[0]] 121 | OutputWhole[Coord[0]]=cv2.rectangle(Rgb,(Coord[2],Coord[1]),(Coord[5],Coord[4]),color=color,thickness=2)[:,:,0] 122 | Rgb[:,:,0]=OutputWhole[Coord[3]] 123 | OutputWhole[Coord[3]]=cv2.rectangle(Rgb,(Coord[2],Coord[1]),(Coord[5],Coord[4]),color=color,thickness=2)[:,:,0] 124 | 125 | Rgb=np.zeros([OutputWhole.shape[0],OutputWhole.shape[1],3],dtype=np.uint8) 126 | Rgb[:,:,0]=OutputWhole[:,:,Coord[2]] 127 | OutputWhole[:,:,Coord[2]]=cv2.rectangle(Rgb,(Coord[1],Coord[0]),(Coord[4],Coord[3]),color=color,thickness=2)[:,:,0] 128 | Rgb[:,:,0]=OutputWhole[:,:,Coord[5]] 129 | OutputWhole[:,:,Coord[5]]=cv2.rectangle(Rgb,(Coord[1],Coord[0]),(Coord[4],Coord[3]),color=color,thickness=2)[:,:,0] 130 | 131 | Rgb=np.zeros([OutputWhole.shape[0],OutputWhole.shape[2],3],dtype=np.uint8) 132 | Rgb[:,:,0]=OutputWhole[:,Coord[1],:] 133 | OutputWhole[:,Coord[1],:]=cv2.rectangle(Rgb,(Coord[2],Coord[0]),(Coord[5],Coord[3]),color=color,thickness=2)[:,:,0] 134 | Rgb[:,:,0]=OutputWhole[:,Coord[4],:] 135 | OutputWhole[:,Coord[4],:]=cv2.rectangle(Rgb,(Coord[2],Coord[0]),(Coord[5],Coord[3]),color=color,thickness=2)[:,:,0] 136 | #Save mhds 137 | OutputWhole=sitk.GetImageFromArray(OutputWhole) 138 | OutputWhole.SetSpacing(opt.TO_SPACING) 139 | if os.path.exists('./Output/'+Patient)==False: 140 | os.makedirs('./Output/'+Patient) 141 | sitk.WriteImage(OutputWhole,'./Output/'+Patient+'/Pred_'+opt.TAG+'.mhd') 142 | sitk.WriteImage(OutputWhole1,'./Output/'+Patient+'/PredRegion_'+opt.TAG+'.mhd') 143 | sitk.WriteImage(OutputWhole2,'./Output/'+Patient+'/PredContour'+opt.TAG+'.mhd') 144 | return Loss,len(RoIs) 145 | def ToTensor(input): 146 | return 0 147 | if __name__=='__main__': 148 | lr=0.0001 149 | Model=RU_Net(opt) 150 | Model=Model.to(opt.GPU) 151 | 152 | optimizer1 = optim.Adam(list(Model.GlobalImageEncoder.parameters()),lr=lr,amsgrad=True) 153 | 154 | optimizer2 = optim.Adam(list(Model.GlobalImageEncoder.parameters())+\ 155 | list(Model.LocalRegionDecoder.parameters()),lr=lr,amsgrad=True) 156 | 157 | TrainPatient=os.listdir(opt.DATA_ROOT+'Train') 158 | ValPatient=os.listdir(opt.DATA_ROOT+'Valid') 159 | TestPatient=os.listdir(opt.DATA_ROOT+'Test') 160 | NumTrain=len(TrainPatient) 161 | NumTest=len(TestPatient) 162 | NumVal=len(ValPatient) 163 | 164 | if not opt.TEST_ONLY: 165 | try: 166 | Model.load_state_dict(torch.load(opt.WEIGHT_PATH)) 167 | print('Weights Loaded!') 168 | except: 169 | #Train Global Image Encoder and RoI locator 170 | for epoch in range(40): 171 | Model.train() 172 | for iteration in range(NumTrain): 173 | Model.train()# 174 | Patient=TrainPatient[random.randint(0,NumTrain-1)] 175 | Image,LabelRegion,LabelContour,Shape,MaximumBbox=DataLoader(Patient,opt,'Train') 176 | Label=LabelRegion 177 | optimizer1.zero_grad() 178 | PredSeg=Model.forward_RoI_Loc(Image,LabelRegion)#Model.train_forward(Image,LabelRegion,LabelContour,UseRoI=True) 179 | LossG=MultiClassDiceLossFunc(PredSeg[0],PredSeg[1]) 180 | LossAll=LossG 181 | LossAll.backward() 182 | optimizer1.step() 183 | LossG=LossG.to('cpu').detach().numpy() 184 | print('loss={g=',LossG,'}') 185 | Loss=[] 186 | torch.save(Model.state_dict(), opt.WEIGHT_PATH) 187 | 188 | #Jointly train Global Image Encoder, RoI locator and Local Region Decoder 189 | Lowest=1 190 | for epoch in range(opt.MAX_EPOCHS): 191 | print('Epoch ',str(epoch),'/'+str(opt.MAX_EPOCHS)) 192 | Model.train()#set_training(True) 193 | for iteration in range(NumTrain): 194 | Patient=TrainPatient[random.randint(0,NumTrain-1)] 195 | Image,LabelRegion,LabelContour,Shape,MaximumBbox=DataLoader(Patient,opt,'Train') 196 | optimizer2.zero_grad() 197 | PredSeg=Model.TrainForward(Image,LabelRegion,LabelContour) 198 | LossG=MultiClassDiceLossFunc(PredSeg[-1][0],PredSeg[-1][1]) 199 | LossR=RoIDiceLossFunc(PredSeg[0],PredSeg[2]) 200 | LossC=RoIDiceLossFunc(PredSeg[1],PredSeg[3]) 201 | CWeight=1.0 202 | LossAll=LossG+LossR+CWeight*LossC 203 | LossAll.backward() 204 | optimizer2.step() 205 | LossG=LossG.to('cpu').detach().numpy() 206 | LossR=LossR.to('cpu').detach().numpy() 207 | LossC=LossC.to('cpu').detach().numpy() 208 | print('loss={g=',LossG,',r=',LossR,',c=',LossC,'}') 209 | Loss=[] 210 | Model.eval()#set_training(False) 211 | 212 | #Model selection according to Global Dice 213 | for iteration in range(NumVal): 214 | Patient=ValPatient[iteration] 215 | Loss_temp,NumRoIs=Predict(Patient,'Val') 216 | Loss+=[Loss_temp] 217 | print(Patient,' Loss=',Loss_temp) 218 | Loss=np.mean(np.array(Loss)) 219 | if Loss