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17.9125 8.46625C18.6138 9.23625 19.04 10.2125 19.04 11.4225C19.04 15.6437 16.4688 16.5787 14.0213 16.8537C14.42 17.1975 14.7638 17.8575 14.7638 18.8887C14.7638 20.36 14.75 21.5425 14.75 21.9137C14.75 22.2025 14.9563 22.5462 15.5063 22.4362C19.8513 20.9787 23 16.8537 23 12C23 5.9225 18.0775 1 12 1Z"></path> </svg> </a> <div class="flex-1 flex-order-2 text-right"> <a href="/login?return_to=https%3A%2F%2Fgithub.com%2FShawnBIT%2FUNet-family" class="HeaderMenu-link HeaderMenu-button d-inline-flex d-lg-none flex-order-1 f5 no-underline border color-border-default rounded-2 px-2 py-1 color-fg-inherit js-prevent-focus-on-mobile-nav" data-hydro-click="{"event_type":"authentication.click","payload":{"location_in_page":"site header menu","repository_id":null,"auth_type":"SIGN_UP","originating_url":"https://github.com/ShawnBIT/UNet-family","user_id":null}}" data-hydro-click-hmac="c6ac8c1e09cc3c01354f6ba20bb5840d8475fe15ed6bb475153a1eaad598d7b2" data-analytics-event="{"category":"Marketing nav","action":"click to Sign in","label":"ref_page:Marketing;ref_cta:Sign in;ref_loc:Header"}" > Sign in </a> </div> </div> <div class="HeaderMenu js-header-menu height-fit position-lg-relative d-lg-flex flex-column flex-auto top-0"> <div class="HeaderMenu-wrapper d-flex flex-column flex-self-start flex-lg-row flex-auto rounded rounded-lg-0"> <nav class="HeaderMenu-nav" aria-label="Global"> <ul class="d-lg-flex list-style-none"> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Product <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down 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data-view-component="true" class="octicon octicon-copilot color-fg-subtle mr-3"> <path d="M23.922 16.992c-.861 1.495-5.859 5.023-11.922 5.023-6.063 0-11.061-3.528-11.922-5.023A.641.641 0 0 1 0 16.736v-2.869a.841.841 0 0 1 .053-.22c.372-.935 1.347-2.292 2.605-2.656.167-.429.414-1.055.644-1.517a10.195 10.195 0 0 1-.052-1.086c0-1.331.282-2.499 1.132-3.368.397-.406.89-.717 1.474-.952 1.399-1.136 3.392-2.093 6.122-2.093 2.731 0 4.767.957 6.166 2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 1.211.175 1.926.558 2.361.365.414 1.084.751 2.657.751 1.21 0 1.902-.394 2.344-.938.475-.584.742-1.44.878-2.497Z"></path><path d="M14.5 14.25a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Zm-5 0a1 1 0 0 1 1 1v2a1 1 0 0 1-2 0v-2a1 1 0 0 1 1-1Z"></path> </svg> <div> <div class="color-fg-default h4">GitHub Copilot</div> Write better code with AI </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" 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</a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"actions","context":"product","tag":"link","label":"actions_link_product_navbar"}" href="https://github.com/features/actions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-workflow color-fg-subtle mr-3"> <path d="M1 3a2 2 0 0 1 2-2h6.5a2 2 0 0 1 2 2v6.5a2 2 0 0 1-2 2H7v4.063C7 16.355 7.644 17 8.438 17H12.5v-2.5a2 2 0 0 1 2-2H21a2 2 0 0 1 2 2V21a2 2 0 0 1-2 2h-6.5a2 2 0 0 1-2-2v-2.5H8.437A2.939 2.939 0 0 1 5.5 15.562V11.5H3a2 2 0 0 1-2-2Zm2-.5a.5.5 0 0 0-.5.5v6.5a.5.5 0 0 0 .5.5h6.5a.5.5 0 0 0 .5-.5V3a.5.5 0 0 0-.5-.5ZM14.5 14a.5.5 0 0 0-.5.5V21a.5.5 0 0 0 .5.5H21a.5.5 0 0 0 .5-.5v-6.5a.5.5 0 0 0-.5-.5Z"></path> </svg> <div> <div class="color-fg-default h4">Actions</div> Automate any 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1.75h-9.69l-3.573 3.573A1.458 1.458 0 0 1 5 21.043V18.5H3.25a1.75 1.75 0 0 1-1.75-1.75ZM3.25 4a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h2.5a.75.75 0 0 1 .75.75v3.19l3.72-3.72a.749.749 0 0 1 .53-.22h10a.25.25 0 0 0 .25-.25V4.25a.25.25 0 0 0-.25-.25Z"></path> </svg> <div> <div class="color-fg-default h4">Code Review</div> Manage code changes </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description pb-lg-3" data-analytics-event="{"location":"navbar","action":"discussions","context":"product","tag":"link","label":"discussions_link_product_navbar"}" href="https://github.com/features/discussions"> <svg aria-hidden="true" height="24" viewBox="0 0 24 24" version="1.1" width="24" data-view-component="true" class="octicon octicon-comment-discussion color-fg-subtle mr-3"> <path d="M1.75 1h12.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 14.25 14H8.061l-2.574 2.573A1.458 1.458 0 0 1 3 15.543V14H1.75A1.75 1.75 0 0 1 0 12.25v-9.5C0 1.784.784 1 1.75 1ZM1.5 2.75v9.5c0 .138.112.25.25.25h2a.75.75 0 0 1 .75.75v2.19l2.72-2.72a.749.749 0 0 1 .53-.22h6.5a.25.25 0 0 0 .25-.25v-9.5a.25.25 0 0 0-.25-.25H1.75a.25.25 0 0 0-.25.25Z"></path><path d="M22.5 8.75a.25.25 0 0 0-.25-.25h-3.5a.75.75 0 0 1 0-1.5h3.5c.966 0 1.75.784 1.75 1.75v9.5A1.75 1.75 0 0 1 22.25 20H21v1.543a1.457 1.457 0 0 1-2.487 1.03L15.939 20H10.75A1.75 1.75 0 0 1 9 18.25v-1.465a.75.75 0 0 1 1.5 0v1.465c0 .138.112.25.25.25h5.5a.75.75 0 0 1 .53.22l2.72 2.72v-2.19a.75.75 0 0 1 .75-.75h2a.25.25 0 0 0 .25-.25v-9.5Z"></path> </svg> <div> <div class="color-fg-default h4">Discussions</div> Collaborate outside of code </div> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"code_search","context":"product","tag":"link","label":"code_search_link_product_navbar"}" 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12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Solutions <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 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Link--secondary" data-analytics-event="{"location":"navbar","action":"view_all_industries","context":"solutions","tag":"link","label":"view_all_industries_link_solutions_navbar"}" href="/solutions/industry"> View all industries </a></li> </ul> </div> </div> <div class="HeaderMenu-trailing-link rounded-bottom-2 flex-shrink-0 mt-lg-4 px-lg-4 py-4 py-lg-3 f5 text-semibold"> <a href="/solutions"> View all solutions <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-right HeaderMenu-trailing-link-icon"> <path d="M6.22 3.22a.75.75 0 0 1 1.06 0l4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L9.94 8 6.22 4.28a.75.75 0 0 1 0-1.06Z"></path> </svg> </a> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Resources <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 d-lg-flex flex-wrap dropdown-menu-wide"> <div class="HeaderMenu-column px-lg-4 border-lg-right mb-4 mb-lg-0 pr-lg-7"> <div class="border-bottom pb-3 pb-lg-0 border-lg-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="resources-topics-heading">Topics</span> <ul 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data-view-component="true" class="octicon octicon-link-external HeaderMenu-external-icon color-fg-subtle"> <path d="M3.75 2h3.5a.75.75 0 0 1 0 1.5h-3.5a.25.25 0 0 0-.25.25v8.5c0 .138.112.25.25.25h8.5a.25.25 0 0 0 .25-.25v-3.5a.75.75 0 0 1 1.5 0v3.5A1.75 1.75 0 0 1 12.25 14h-8.5A1.75 1.75 0 0 1 2 12.25v-8.5C2 2.784 2.784 2 3.75 2Zm6.854-1h4.146a.25.25 0 0 1 .25.25v4.146a.25.25 0 0 1-.427.177L13.03 4.03 9.28 7.78a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042l3.75-3.75-1.543-1.543A.25.25 0 0 1 10.604 1Z"></path> </svg> </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"executive_insights","context":"resources","tag":"link","label":"executive_insights_link_resources_navbar"}" href="https://github.com/solutions/executive-insights"> Executive Insights </a></li> </ul> </div> </div> </div> </li> <li class="HeaderMenu-item position-relative flex-wrap flex-justify-between flex-items-center d-block d-lg-flex flex-lg-nowrap flex-lg-items-center js-details-container js-header-menu-item"> <button type="button" class="HeaderMenu-link border-0 width-full width-lg-auto px-0 px-lg-2 py-lg-2 no-wrap d-flex flex-items-center flex-justify-between js-details-target" aria-expanded="false"> Open Source <svg opacity="0.5" aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-chevron-down HeaderMenu-icon ml-1"> <path d="M12.78 5.22a.749.749 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.06 0L3.22 6.28a.749.749 0 1 1 1.06-1.06L8 8.939l3.72-3.719a.749.749 0 0 1 1.06 0Z"></path> </svg> </button> <div class="HeaderMenu-dropdown dropdown-menu rounded m-0 p-0 pt-2 pt-lg-4 position-relative position-lg-absolute left-0 left-lg-n3 pb-2 pb-lg-4 px-lg-4"> <div class="HeaderMenu-column"> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"github_sponsors","context":"open_source","tag":"link","label":"github_sponsors_link_open_source_navbar"}" href="/sponsors"> <div> <div class="color-fg-default h4">GitHub Sponsors</div> Fund open source developers </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 pb-lg-3 mb-3 mb-lg-0 mb-lg-3"> <ul class="list-style-none f5" > <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary d-flex flex-items-center Link--has-description" data-analytics-event="{"location":"navbar","action":"the_readme_project","context":"open_source","tag":"link","label":"the_readme_project_link_open_source_navbar"}" href="https://github.com/readme"> <div> <div class="color-fg-default h4">The ReadME Project</div> GitHub community articles </div> </a></li> </ul> </div> <div class="border-bottom pb-3 pb-lg-0 border-bottom-0"> <span class="d-block h4 color-fg-default my-1" id="open-source-repositories-heading">Repositories</span> <ul class="list-style-none f5" aria-labelledby="open-source-repositories-heading"> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"topics","context":"open_source","tag":"link","label":"topics_link_open_source_navbar"}" href="https://github.com/topics"> Topics </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" data-analytics-event="{"location":"navbar","action":"trending","context":"open_source","tag":"link","label":"trending_link_open_source_navbar"}" href="https://github.com/trending"> Trending </a></li> <li> <a class="HeaderMenu-dropdown-link d-block no-underline position-relative py-2 Link--secondary" 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2.093.584.235 1.077.546 1.474.952.85.869 1.132 2.037 1.132 3.368 0 .368-.014.733-.052 1.086.23.462.477 1.088.644 1.517 1.258.364 2.233 1.721 2.605 2.656a.832.832 0 0 1 .053.22v2.869a.641.641 0 0 1-.078.256ZM12.172 11h-.344a4.323 4.323 0 0 1-.355.508C10.703 12.455 9.555 13 7.965 13c-1.725 0-2.989-.359-3.782-1.259a2.005 2.005 0 0 1-.085-.104L4 11.741v6.585c1.435.779 4.514 2.179 8 2.179 3.486 0 6.565-1.4 8-2.179v-6.585l-.098-.104s-.033.045-.085.104c-.793.9-2.057 1.259-3.782 1.259-1.59 0-2.738-.545-3.508-1.492a4.323 4.323 0 0 1-.355-.508h-.016.016Zm.641-2.935c.136 1.057.403 1.913.878 2.497.442.544 1.134.938 2.344.938 1.573 0 2.292-.337 2.657-.751.384-.435.558-1.15.558-2.361 0-1.14-.243-1.847-.705-2.319-.477-.488-1.319-.862-2.824-1.025-1.487-.161-2.192.138-2.533.529-.269.307-.437.808-.438 1.578v.021c0 .265.021.562.063.893Zm-1.626 0c.042-.331.063-.628.063-.894v-.02c-.001-.77-.169-1.271-.438-1.578-.341-.391-1.046-.69-2.533-.529-1.505.163-2.347.537-2.824 1.025-.462.472-.705 1.179-.705 2.319 0 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seriously.</p> <textarea name="feedback" class="form-control width-full mb-2" style="height: 120px" id="feedback"></textarea> <input name="include_email" id="include_email" aria-label="Include my email address so I can be contacted" class="form-control mr-2" type="checkbox"> <label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" 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data-target="react-partial.embeddedData">{"props":{"initialPayload":{"allShortcutsEnabled":false,"path":"/","repo":{"id":162696927,"defaultBranch":"master","name":"UNet-family","ownerLogin":"ShawnBIT","currentUserCanPush":false,"isFork":false,"isEmpty":false,"createdAt":"2018-12-21T09:51:06.000Z","ownerAvatar":"https://avatars.githubusercontent.com/u/20378492?v=4","public":true,"private":false,"isOrgOwned":false},"currentUser":null,"refInfo":{"name":"master","listCacheKey":"v0:1545385926.0","canEdit":false,"refType":"branch","currentOid":"561bcaa5808cd15a057bcfcf54c95ff24d54406b"},"tree":{"items":[{"name":"networks","path":"networks","contentType":"directory"},{"name":"pictures","path":"pictures","contentType":"directory"},{"name":"README.md","path":"README.md","contentType":"file"},{"name":"layers.py","path":"layers.py","contentType":"file"},{"name":"utils.py","path":"utils.py","contentType":"file"}],"templateDirectorySuggestionUrl":null,"readme":null,"totalCount":5,"showBranchInfobar":false},"fileTree":null,"fileTreeProcessingTime":null,"foldersToFetch":[],"treeExpanded":false,"symbolsExpanded":false,"isOverview":true,"overview":{"banners":{"shouldRecommendReadme":false,"isPersonalRepo":false,"showUseActionBanner":false,"actionSlug":null,"actionId":null,"showProtectBranchBanner":false,"publishBannersInfo":{"dismissActionNoticePath":"/settings/dismiss-notice/publish_action_from_repo","releasePath":"/ShawnBIT/UNet-family/releases/new?marketplace=true","showPublishActionBanner":false},"interactionLimitBanner":null,"showInvitationBanner":false,"inviterName":null,"actionsMigrationBannerInfo":{"releaseTags":[],"showImmutableActionsMigrationBanner":false,"initialMigrationStatus":null}},"codeButton":{"contactPath":"/contact","isEnterprise":false,"local":{"protocolInfo":{"httpAvailable":true,"sshAvailable":null,"httpUrl":"https://github.com/ShawnBIT/UNet-family.git","showCloneWarning":null,"sshUrl":null,"sshCertificatesRequired":null,"sshCertificatesAvailable":null,"ghCliUrl":"gh 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dir=\"auto\"\u003eIntroduction\u003c/h1\u003e\u003ca id=\"user-content-introduction\" class=\"anchor\" aria-label=\"Permalink: Introduction\" href=\"#introduction\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eSince 2015, \u003cstrong\u003eUNet\u003c/strong\u003e has made major breakthroughs in the medical image segmentation , opening the era of deep learning. Later researchers have made a lot of improvements on the basis of UNet in order to improve the performance of semantic segmentation.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eIn this project, we have compiled the semantic segmentation models related to UNet(\u003cstrong\u003eUNet family\u003c/strong\u003e) in recent years.\nMy implementation is mainly based on \u003cstrong\u003epytorch\u003c/strong\u003e, and other implementations are collected from author of original paper or excellent repositories. For the record, this project is still \u003cstrong\u003eunder construction\u003c/strong\u003e. If you have any advice or question, please raise an issue or contact me from email.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFurthermore, why does the UNet neural network perform well in medical image segmentation?\nYou can figure it out from \u003ca href=\"https://www.zhihu.com/question/269914775/answer/586501606\" rel=\"nofollow\"\u003emy answer\u003c/a\u003e in Zhihu.\u003c/p\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\n \u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/ShawnBIT/UNet-family/blob/master/pictures/unet.png\"\u003e\u003cimg src=\"https://github.com/ShawnBIT/UNet-family/raw/master/pictures/unet.png\" width=\"1000\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e \n\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eUNet-family\u003c/h1\u003e\u003ca id=\"user-content-unet-family\" class=\"anchor\" aria-label=\"Permalink: UNet-family\" href=\"#unet-family\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2015\u003c/h2\u003e\u003ca id=\"user-content-2015\" class=\"anchor\" aria-label=\"Permalink: 2015\" href=\"#2015\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eU-Net: Convolutional Networks for Biomedical Image Segmentation (MICCAI) [\u003ca href=\"https://arxiv.org/pdf/1505.04597.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/ShawnBIT/UNet-family/blob/master/networks/UNet.py\"\u003emy-pytorch\u003c/a\u003e][\u003ca href=\"https://github.com/zhixuhao/unet\"\u003ekeras\u003c/a\u003e]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2016\u003c/h2\u003e\u003ca id=\"user-content-2016\" class=\"anchor\" aria-label=\"Permalink: 2016\" href=\"#2016\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eV-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation [\u003ca href=\"http://campar.in.tum.de/pub/milletari2016Vnet/milletari2016Vnet.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/faustomilletari/VNet\"\u003ecaffe\u003c/a\u003e][\u003ca href=\"https://github.com/mattmacy/vnet.pytorch\"\u003epytorch\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation [\u003ca href=\"https://arxiv.org/pdf/1606.06650.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e][\u003ca href=\"https://github.com/wolny/pytorch-3dunet\"\u003epytorch\u003c/a\u003e]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2017\u003c/h2\u003e\u003ca id=\"user-content-2017\" class=\"anchor\" aria-label=\"Permalink: 2017\" href=\"#2017\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eH-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes (IEEE Transactions on Medical Imaging)[\u003ca href=\"https://arxiv.org/pdf/1709.07330.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e][\u003ca href=\"https://github.com/xmengli999/H-DenseUNet\"\u003ekeras\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eGP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network (MICCAI) [\u003ca href=\"https://arxiv.org/pdf/1705.07999.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2018\u003c/h2\u003e\u003ca id=\"user-content-2018\" class=\"anchor\" aria-label=\"Permalink: 2018\" href=\"#2018\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eUNet++: A Nested U-Net Architecture for Medical Image Segmentation (MICCAI) [\u003ca href=\"https://arxiv.org/pdf/1807.10165.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e][\u003ca href=\"https://github.com/ShawnBIT/UNet-family/blob/master/networks/UNet_Nested.py\"\u003emy-pytorch\u003c/a\u003e][\u003ca href=\"https://github.com/MrGiovanni/UNetPlusPlus\"\u003ekeras\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eMDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation [\u003ca href=\"https://arxiv.org/pdf/1812.00352.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eDUNet: A deformable network for retinal vessel segmentation [\u003ca href=\"https://arxiv.org/pdf/1811.01206.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eRA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans [\u003ca href=\"https://arxiv.org/pdf/1811.01328.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eDense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities [\u003ca href=\"https://arxiv.org/pdf/1810.07003.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eStacked Dense U-Nets with Dual Transformers for Robust Face Alignment [\u003ca href=\"https://arxiv.org/pdf/1812.01936.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eProstate Segmentation using 2D Bridged U-net [\u003ca href=\"https://arxiv.org/pdf/1807.04459.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003ennU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation [\u003ca href=\"https://arxiv.org/pdf/1809.10486.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e][\u003ca href=\"https://github.com/MIC-DKFZ/nnUNet\"\u003epytorch\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eSUNet: a deep learning architecture for acute stroke lesion segmentation and\noutcome prediction in multimodal MRI [\u003ca href=\"https://arxiv.org/pdf/1810.13304.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eIVD-Net: Intervertebral disc localization and segmentation in MRI with a multi-modal UNet [\u003ca href=\"https://arxiv.org/pdf/1811.08305.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eLADDERNET: Multi-Path Networks Based on U-Net for Medical Image Segmentation [\u003ca href=\"https://arxiv.org/pdf/1810.07810.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e][\u003ca href=\"https://github.com/juntang-zhuang/LadderNet\"\u003epytorch\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eGlioma Segmentation with Cascaded Unet [\u003ca href=\"https://arxiv.org/pdf/1810.04008.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eAttention U-Net: Learning Where to Look for the Pancreas [\u003ca href=\"https://arxiv.org/pdf/1804.03999.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eRecurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation [\u003ca href=\"https://arxiv.org/pdf/1802.06955.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eConcurrent Spatial and Channel ‘Squeeze \u0026amp; Excitation’ in Fully Convolutional Networks \u003ca href=\"https://arxiv.org/pdf/1803.02579.pdf\" rel=\"nofollow\"\u003e[paper]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eA Probabilistic U-Net for Segmentation of Ambiguous Images (NIPS) [\u003ca href=\"https://arxiv.org/pdf/1806.05034.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/SimonKohl/probabilistic_unet\"\u003etensorflow\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eAnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy [\u003ca href=\"https://arxiv.org/pdf/1808.05238.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e3D RoI-aware U-Net for Accurate and Efficient Colorectal Cancer Segmentation [\u003ca href=\"https://arxiv.org/pdf/1806.10342.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e][\u003ca href=\"https://github.com/huangyjhust/3D-RU-Net\"\u003epytorch\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eDetection and Delineation of Acute Cerebral Infarct on DWI Using Weakly Supervised Machine Learning (Y-Net) (MICCAI) [\u003ca href=\"https://link.springer.com/content/pdf/10.1007%2F978-3-030-00931-1.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e](Page 82)\u003c/li\u003e\n\u003cli\u003eFully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal [\u003ca href=\"https://arxiv.org/pdf/1808.10848.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2019\u003c/h2\u003e\u003ca id=\"user-content-2019\" class=\"anchor\" aria-label=\"Permalink: 2019\" href=\"#2019\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eMultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation [\u003ca href=\"https://arxiv.org/pdf/1902.04049v1.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e][\u003ca href=\"https://github.com/nibtehaz/MultiResUNet\"\u003ekeras\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eU-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instrument [\u003ca href=\"https://arxiv.org/pdf/1902.08994.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eProbability Map Guided Bi-directional Recurrent UNet for Pancreas Segmentation [\u003ca href=\"https://arxiv.org/pdf/1903.00923.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eCE-Net: Context Encoder Network for 2D Medical Image Segmentation [\u003ca href=\"https://arxiv.org/pdf/1903.02740.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e][\u003ca href=\"https://github.com/Guzaiwang/CE-Net\"\u003epytorch\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eGraph U-Net [\u003ca href=\"https://openreview.net/pdf?id=HJePRoAct7\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eA Novel Focal Tversky Loss Function with Improved Attention U-Net for Lesion Segmentation (ISBI) [\u003ca href=\"https://arxiv.org/pdf/1810.07842.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling [\u003ca href=\"https://arxiv.org/pdf/1903.05631.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eConnection Sensitive Attention U-NET for Accurate Retinal Vessel Segmentation [\u003ca href=\"https://arxiv.org/pdf/1903.05558.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eCIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation [\u003ca href=\"https://arxiv.org/pdf/1903.05358.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eW-Net: Reinforced U-Net for Density Map Estimation [\u003ca href=\"https://arxiv.org/pdf/1903.11249.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eAutomated Segmentation of Pulmonary Lobes using Coordination-guided Deep Neural Networks (ISBI oral) [\u003ca href=\"https://arxiv.org/pdf/1904.09106.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eU2-Net: A Bayesian U-Net Model with Epistemic Uncertainty Feedback for Photoreceptor Layer Segmentation in Pathological OCT Scans [\u003ca href=\"https://arxiv.org/pdf/1901.07929.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eScleraSegNet: an Improved U-Net Model with Attention for Accurate Sclera Segmentation (ICB Honorable Mention Paper Award) [\u003ca href=\"https://github.com/ShawnBIT/Paper-Reading/blob/master/ScleraSegNet.pdf\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eAHCNet: An Application of Attention Mechanism and Hybrid Connection for Liver Tumor Segmentation in CT Volumes [\u003ca href=\"https://github.com/ShawnBIT/Paper-Reading/blob/master/AHCNet.pdf\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eA Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities [\u003ca href=\"https://arxiv.org/pdf/1905.13077.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eRecurrent U-Net for Resource-Constrained Segmentation [\u003ca href=\"https://arxiv.org/pdf/1906.04913.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eMFP-Unet: A Novel Deep Learning Based Approach for Left Ventricle Segmentation in Echocardiography [\u003ca href=\"https://arxiv.org/pdf/1906.10486.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eA Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation (MICCAI 2019) [\u003ca href=\"https://arxiv.org/pdf/1906.06148.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e][\u003ca href=\"https://github.com/RobinBruegger/PartiallyReversibleUnet\"\u003epytorch\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data [\u003ca href=\"https://arxiv.org/pdf/1904.00592v2.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eA multi-task U-net for segmentation with lazy labels [\u003ca href=\"https://arxiv.org/pdf/1906.12177.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eRAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments [\u003ca href=\"http://xxx.itp.ac.cn/pdf/1909.10360v1\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e3D U2-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation (MICCAI 2019) [\u003ca href=\"https://arxiv.org/pdf/1909.06012.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] [\u003ca href=\"https://github.com/huangmozhilv/u2net_torch/\"\u003epytorch\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eSegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation (MICCAI 2019) [\u003ca href=\"https://arxiv.org/pdf/1909.05962.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003e3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI [\u003ca href=\"https://arxiv.org/pdf/1904.03355.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e][\u003ca href=\"https://github.com/China-LiuXiaopeng/BraTS-DMFNet\"\u003epytorch\u003c/a\u003e] (MICCAI 2019)\u003c/li\u003e\n\u003cli\u003eThe Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN [\u003ca href=\"https://arxiv.org/pdf/1910.13681.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eRecurrent U-Net for Resource-Constrained Segmentation [\u003ca href=\"http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Recurrent_U-Net_for_Resource-Constrained_Segmentation_ICCV_2019_paper.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e] (ICCV 2019)\u003c/li\u003e\n\u003cli\u003eSiamese U-Net with Healthy Template for Accurate Segmentation of Intracranial Hemorrhage (MICCAI 2019)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e2020\u003c/h2\u003e\u003ca id=\"user-content-2020\" class=\"anchor\" aria-label=\"Permalink: 2020\" href=\"#2020\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003eU^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection (Pattern Recognition 2020) [\u003ca href=\"https://arxiv.org/pdf/2005.09007v1.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e][\u003ca href=\"https://github.com/NathanUA/U-2-Net\"\u003epytorch\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eUNET 3+: A Full-Scale Connected UNet for Medical Image Segmentation (ICASSP 2020) [\u003ca href=\"https://arxiv.org/pdf/2004.08790.pdf\" rel=\"nofollow\"\u003epaper\u003c/a\u003e][\u003ca href=\"https://github.com/ZJUGiveLab/UNet-Version\"\u003epytorch\u003c/a\u003e]\u003c/li\u003e\n\u003c/ul\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch1 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eReference\u003c/h1\u003e\u003ca id=\"user-content-reference\" class=\"anchor\" aria-label=\"Permalink: Reference\" href=\"#reference\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cul dir=\"auto\"\u003e\n\u003cli\u003e\u003ca href=\"https://github.com/ozan-oktay/Attention-Gated-Networks\"\u003ehttps://github.com/ozan-oktay/Attention-Gated-Networks\u003c/a\u003e (Our model style mainly refers to this 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fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m11.28 3.22 4.25 4.25a.75.75 0 0 1 0 1.06l-4.25 4.25a.749.749 0 0 1-1.275-.326.749.749 0 0 1 .215-.734L13.94 8l-3.72-3.72a.749.749 0 0 1 .326-1.275.749.749 0 0 1 .734.215Zm-6.56 0a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042L2.06 8l3.72 3.72a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L.47 8.53a.75.75 0 0 1 0-1.06Z"></path></svg></span><span data-component="text" class="prc-Button-Label-pTQ3x">Code</span><span data-component="trailingVisual" class="prc-Button-Visual-2epfX prc-Button-VisualWrap-Db-eB"><svg aria-hidden="true" focusable="false" class="octicon octicon-triangle-down" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="m4.427 7.427 3.396 3.396a.25.25 0 0 0 .354 0l3.396-3.396A.25.25 0 0 0 11.396 7H4.604a.25.25 0 0 0-.177.427Z"></path></svg></span></span></button><div 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class="react-directory-row-commit-cell"><div class="Skeleton Skeleton--text"> </div></td><td><div class="Skeleton Skeleton--text"> </div></td></tr><tr class="react-directory-row undefined" id="folder-row-3"><td class="react-directory-row-name-cell-small-screen" colSpan="2"><div class="react-directory-filename-column"><svg aria-hidden="true" focusable="false" class="octicon octicon-file color-fg-muted" viewBox="0 0 16 16" width="16" height="16" fill="currentColor" display="inline-block" overflow="visible" style="vertical-align:text-bottom"><path d="M2 1.75C2 .784 2.784 0 3.75 0h6.586c.464 0 .909.184 1.237.513l2.914 2.914c.329.328.513.773.513 1.237v9.586A1.75 1.75 0 0 1 13.25 16h-9.5A1.75 1.75 0 0 1 2 14.25Zm1.75-.25a.25.25 0 0 0-.25.25v12.5c0 .138.112.25.25.25h9.5a.25.25 0 0 0 .25-.25V6h-2.75A1.75 1.75 0 0 1 9 4.25V1.5Zm6.75.062V4.25c0 .138.112.25.25.25h2.688l-.011-.013-2.914-2.914-.013-.011Z"></path></svg><div class="overflow-hidden"><div class="react-directory-filename-cell"><div 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js-snippet-clipboard-copy-unpositioned undefined" data-hpc="true"><article class="markdown-body entry-content container-lg" itemprop="text"><div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Introduction</h1><a id="user-content-introduction" class="anchor" aria-label="Permalink: Introduction" href="#introduction"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Since 2015, <strong>UNet</strong> has made major breakthroughs in the medical image segmentation , opening the era of deep learning. Later researchers have made a lot of improvements on the basis of UNet in order to improve the performance of semantic segmentation.</p> <p dir="auto">In this project, we have compiled the semantic segmentation models related to UNet(<strong>UNet family</strong>) in recent years. My implementation is mainly based on <strong>pytorch</strong>, and other implementations are collected from author of original paper or excellent repositories. For the record, this project is still <strong>under construction</strong>. If you have any advice or question, please raise an issue or contact me from email.</p> <p dir="auto">Furthermore, why does the UNet neural network perform well in medical image segmentation? You can figure it out from <a href="https://www.zhihu.com/question/269914775/answer/586501606" rel="nofollow">my answer</a> in Zhihu.</p> <p align="center" dir="auto"> <a target="_blank" rel="noopener noreferrer" href="https://github.com/ShawnBIT/UNet-family/blob/master/pictures/unet.png"><img src="https://github.com/ShawnBIT/UNet-family/raw/master/pictures/unet.png" width="1000" style="max-width: 100%;"></a> </p> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">UNet-family</h1><a id="user-content-unet-family" class="anchor" aria-label="Permalink: UNet-family" href="#unet-family"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2015</h2><a id="user-content-2015" class="anchor" aria-label="Permalink: 2015" href="#2015"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>U-Net: Convolutional Networks for Biomedical Image Segmentation (MICCAI) [<a href="https://arxiv.org/pdf/1505.04597.pdf" rel="nofollow">paper</a>] [<a href="https://github.com/ShawnBIT/UNet-family/blob/master/networks/UNet.py">my-pytorch</a>][<a href="https://github.com/zhixuhao/unet">keras</a>]</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2016</h2><a id="user-content-2016" class="anchor" aria-label="Permalink: 2016" href="#2016"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation [<a href="http://campar.in.tum.de/pub/milletari2016Vnet/milletari2016Vnet.pdf" rel="nofollow">paper</a>] [<a href="https://github.com/faustomilletari/VNet">caffe</a>][<a href="https://github.com/mattmacy/vnet.pytorch">pytorch</a>]</li> <li>3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation [<a href="https://arxiv.org/pdf/1606.06650.pdf" rel="nofollow">paper</a>][<a href="https://github.com/wolny/pytorch-3dunet">pytorch</a>]</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2017</h2><a id="user-content-2017" class="anchor" aria-label="Permalink: 2017" href="#2017"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes (IEEE Transactions on Medical Imaging)[<a href="https://arxiv.org/pdf/1709.07330.pdf" rel="nofollow">paper</a>][<a href="https://github.com/xmengli999/H-DenseUNet">keras</a>]</li> <li>GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network (MICCAI) [<a href="https://arxiv.org/pdf/1705.07999.pdf" rel="nofollow">paper</a>]</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2018</h2><a id="user-content-2018" class="anchor" aria-label="Permalink: 2018" href="#2018"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>UNet++: A Nested U-Net Architecture for Medical Image Segmentation (MICCAI) [<a href="https://arxiv.org/pdf/1807.10165.pdf" rel="nofollow">paper</a>][<a href="https://github.com/ShawnBIT/UNet-family/blob/master/networks/UNet_Nested.py">my-pytorch</a>][<a href="https://github.com/MrGiovanni/UNetPlusPlus">keras</a>]</li> <li>MDU-Net: Multi-scale Densely Connected U-Net for biomedical image segmentation [<a href="https://arxiv.org/pdf/1812.00352.pdf" rel="nofollow">paper</a>]</li> <li>DUNet: A deformable network for retinal vessel segmentation [<a href="https://arxiv.org/pdf/1811.01206.pdf" rel="nofollow">paper</a>]</li> <li>RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans [<a href="https://arxiv.org/pdf/1811.01328.pdf" rel="nofollow">paper</a>]</li> <li>Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities [<a href="https://arxiv.org/pdf/1810.07003.pdf" rel="nofollow">paper</a>]</li> <li>Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment [<a href="https://arxiv.org/pdf/1812.01936.pdf" rel="nofollow">paper</a>]</li> <li>Prostate Segmentation using 2D Bridged U-net [<a href="https://arxiv.org/pdf/1807.04459.pdf" rel="nofollow">paper</a>]</li> <li>nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation [<a href="https://arxiv.org/pdf/1809.10486.pdf" rel="nofollow">paper</a>][<a href="https://github.com/MIC-DKFZ/nnUNet">pytorch</a>]</li> <li>SUNet: a deep learning architecture for acute stroke lesion segmentation and outcome prediction in multimodal MRI [<a href="https://arxiv.org/pdf/1810.13304.pdf" rel="nofollow">paper</a>]</li> <li>IVD-Net: Intervertebral disc localization and segmentation in MRI with a multi-modal UNet [<a href="https://arxiv.org/pdf/1811.08305.pdf" rel="nofollow">paper</a>]</li> <li>LADDERNET: Multi-Path Networks Based on U-Net for Medical Image Segmentation [<a href="https://arxiv.org/pdf/1810.07810.pdf" rel="nofollow">paper</a>][<a href="https://github.com/juntang-zhuang/LadderNet">pytorch</a>]</li> <li>Glioma Segmentation with Cascaded Unet [<a href="https://arxiv.org/pdf/1810.04008.pdf" rel="nofollow">paper</a>]</li> <li>Attention U-Net: Learning Where to Look for the Pancreas [<a href="https://arxiv.org/pdf/1804.03999.pdf" rel="nofollow">paper</a>]</li> <li>Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation [<a href="https://arxiv.org/pdf/1802.06955.pdf" rel="nofollow">paper</a>]</li> <li>Concurrent Spatial and Channel ‘Squeeze & Excitation’ in Fully Convolutional Networks <a href="https://arxiv.org/pdf/1803.02579.pdf" rel="nofollow">[paper]</a></li> <li>A Probabilistic U-Net for Segmentation of Ambiguous Images (NIPS) [<a href="https://arxiv.org/pdf/1806.05034.pdf" rel="nofollow">paper</a>] [<a href="https://github.com/SimonKohl/probabilistic_unet">tensorflow</a>]</li> <li>AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy [<a href="https://arxiv.org/pdf/1808.05238.pdf" rel="nofollow">paper</a>]</li> <li>3D RoI-aware U-Net for Accurate and Efficient Colorectal Cancer Segmentation [<a href="https://arxiv.org/pdf/1806.10342.pdf" rel="nofollow">paper</a>][<a href="https://github.com/huangyjhust/3D-RU-Net">pytorch</a>]</li> <li>Detection and Delineation of Acute Cerebral Infarct on DWI Using Weakly Supervised Machine Learning (Y-Net) (MICCAI) [<a href="https://link.springer.com/content/pdf/10.1007%2F978-3-030-00931-1.pdf" rel="nofollow">paper</a>](Page 82)</li> <li>Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal [<a href="https://arxiv.org/pdf/1808.10848.pdf" rel="nofollow">paper</a>]</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2019</h2><a id="user-content-2019" class="anchor" aria-label="Permalink: 2019" href="#2019"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation [<a href="https://arxiv.org/pdf/1902.04049v1.pdf" rel="nofollow">paper</a>][<a href="https://github.com/nibtehaz/MultiResUNet">keras</a>]</li> <li>U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instrument [<a href="https://arxiv.org/pdf/1902.08994.pdf" rel="nofollow">paper</a>]</li> <li>Probability Map Guided Bi-directional Recurrent UNet for Pancreas Segmentation [<a href="https://arxiv.org/pdf/1903.00923.pdf" rel="nofollow">paper</a>]</li> <li>CE-Net: Context Encoder Network for 2D Medical Image Segmentation [<a href="https://arxiv.org/pdf/1903.02740.pdf" rel="nofollow">paper</a>][<a href="https://github.com/Guzaiwang/CE-Net">pytorch</a>]</li> <li>Graph U-Net [<a href="https://openreview.net/pdf?id=HJePRoAct7" rel="nofollow">paper</a>]</li> <li>A Novel Focal Tversky Loss Function with Improved Attention U-Net for Lesion Segmentation (ISBI) [<a href="https://arxiv.org/pdf/1810.07842.pdf" rel="nofollow">paper</a>]</li> <li>ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling [<a href="https://arxiv.org/pdf/1903.05631.pdf" rel="nofollow">paper</a>]</li> <li>Connection Sensitive Attention U-NET for Accurate Retinal Vessel Segmentation [<a href="https://arxiv.org/pdf/1903.05558.pdf" rel="nofollow">paper</a>]</li> <li>CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation [<a href="https://arxiv.org/pdf/1903.05358.pdf" rel="nofollow">paper</a>]</li> <li>W-Net: Reinforced U-Net for Density Map Estimation [<a href="https://arxiv.org/pdf/1903.11249.pdf" rel="nofollow">paper</a>]</li> <li>Automated Segmentation of Pulmonary Lobes using Coordination-guided Deep Neural Networks (ISBI oral) [<a href="https://arxiv.org/pdf/1904.09106.pdf" rel="nofollow">paper</a>]</li> <li>U2-Net: A Bayesian U-Net Model with Epistemic Uncertainty Feedback for Photoreceptor Layer Segmentation in Pathological OCT Scans [<a href="https://arxiv.org/pdf/1901.07929.pdf" rel="nofollow">paper</a>]</li> <li>ScleraSegNet: an Improved U-Net Model with Attention for Accurate Sclera Segmentation (ICB Honorable Mention Paper Award) [<a href="https://github.com/ShawnBIT/Paper-Reading/blob/master/ScleraSegNet.pdf">paper</a>]</li> <li>AHCNet: An Application of Attention Mechanism and Hybrid Connection for Liver Tumor Segmentation in CT Volumes [<a href="https://github.com/ShawnBIT/Paper-Reading/blob/master/AHCNet.pdf">paper</a>]</li> <li>A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities [<a href="https://arxiv.org/pdf/1905.13077.pdf" rel="nofollow">paper</a>]</li> <li>Recurrent U-Net for Resource-Constrained Segmentation [<a href="https://arxiv.org/pdf/1906.04913.pdf" rel="nofollow">paper</a>]</li> <li>MFP-Unet: A Novel Deep Learning Based Approach for Left Ventricle Segmentation in Echocardiography [<a href="https://arxiv.org/pdf/1906.10486.pdf" rel="nofollow">paper</a>]</li> <li>A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation (MICCAI 2019) [<a href="https://arxiv.org/pdf/1906.06148.pdf" rel="nofollow">paper</a>][<a href="https://github.com/RobinBruegger/PartiallyReversibleUnet">pytorch</a>]</li> <li>ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data [<a href="https://arxiv.org/pdf/1904.00592v2.pdf" rel="nofollow">paper</a>]</li> <li>A multi-task U-net for segmentation with lazy labels [<a href="https://arxiv.org/pdf/1906.12177.pdf" rel="nofollow">paper</a>]</li> <li>RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instruments [<a href="http://xxx.itp.ac.cn/pdf/1909.10360v1" rel="nofollow">paper</a>]</li> <li>3D U2-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation (MICCAI 2019) [<a href="https://arxiv.org/pdf/1909.06012.pdf" rel="nofollow">paper</a>] [<a href="https://github.com/huangmozhilv/u2net_torch/">pytorch</a>]</li> <li>SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation (MICCAI 2019) [<a href="https://arxiv.org/pdf/1909.05962.pdf" rel="nofollow">paper</a>]</li> <li>3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI [<a href="https://arxiv.org/pdf/1904.03355.pdf" rel="nofollow">paper</a>][<a href="https://github.com/China-LiuXiaopeng/BraTS-DMFNet">pytorch</a>] (MICCAI 2019)</li> <li>The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN [<a href="https://arxiv.org/pdf/1910.13681.pdf" rel="nofollow">paper</a>]</li> <li>Recurrent U-Net for Resource-Constrained Segmentation [<a href="http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Recurrent_U-Net_for_Resource-Constrained_Segmentation_ICCV_2019_paper.pdf" rel="nofollow">paper</a>] (ICCV 2019)</li> <li>Siamese U-Net with Healthy Template for Accurate Segmentation of Intracranial Hemorrhage (MICCAI 2019)</li> </ul> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">2020</h2><a id="user-content-2020" class="anchor" aria-label="Permalink: 2020" href="#2020"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li>U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection (Pattern Recognition 2020) [<a href="https://arxiv.org/pdf/2005.09007v1.pdf" rel="nofollow">paper</a>][<a href="https://github.com/NathanUA/U-2-Net">pytorch</a>]</li> <li>UNET 3+: A Full-Scale Connected UNet for Medical Image Segmentation (ICASSP 2020) [<a href="https://arxiv.org/pdf/2004.08790.pdf" rel="nofollow">paper</a>][<a href="https://github.com/ZJUGiveLab/UNet-Version">pytorch</a>]</li> </ul> <div class="markdown-heading" dir="auto"><h1 tabindex="-1" class="heading-element" dir="auto">Reference</h1><a id="user-content-reference" class="anchor" aria-label="Permalink: Reference" href="#reference"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <ul dir="auto"> <li><a href="https://github.com/ozan-oktay/Attention-Gated-Networks">https://github.com/ozan-oktay/Attention-Gated-Networks</a> (Our model style mainly refers to this repository.）</li> </ul> </article></div></div></div></div></div> <!-- --> <!-- --> <script type="application/json" id="__PRIMER_DATA_:R0:__">{"resolvedServerColorMode":"day"}</script></div> </react-partial> <input type="hidden" data-csrf="true" value="Tdc/gTfe4arqN4iVVWC34GsbrxOAJjQUD4vpeaWXuVIUGDk3ePgqCCeKrSfY4J7iJZPiQx5outXcf3PWjbCfHA==" /> </div> <div data-view-component="true" class="Layout-sidebar"> <div class="BorderGrid about-margin" data-pjax> <div class="BorderGrid-row"> <div class="BorderGrid-cell"> <div class="hide-sm hide-md"> <h2 class="mb-3 h4">About</h2> <p class="f4 my-3"> Paper and implementation of UNet-related model. </p> <h3 class="sr-only">Topics</h3> <div class="my-3"> <div class="f6"> <a 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