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Morphology-Driven Deep Watershed Transform for 3D Tooth Segmentation - Centre for Computational Personalized Medicine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sano.science\/research\/morphology-driven-deep-watershed-transform-for-3d-tooth-segmentation\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Morphology-Driven Deep Watershed Transform for 3D Tooth Segmentation\" \/>\n<meta property=\"og:description\" content=\"ODIN Workshop @ MICCAI, 2025\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/research\/morphology-driven-deep-watershed-transform-for-3d-tooth-segmentation\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2026-07-15T09:49:14+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/research\\\/morphology-driven-deep-watershed-transform-for-3d-tooth-segmentation\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/research\\\/morphology-driven-deep-watershed-transform-for-3d-tooth-segmentation\\\/\",\"name\":\"Morphology-Driven Deep Watershed Transform for 3D Tooth Segmentation - 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Centre for Computational Personalized Medicine","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/sano.science\/research\/morphology-driven-deep-watershed-transform-for-3d-tooth-segmentation\/","og_locale":"en_US","og_type":"article","og_title":"Morphology-Driven Deep Watershed Transform for 3D Tooth Segmentation","og_description":"ODIN Workshop @ MICCAI, 2025","og_url":"https:\/\/sano.science\/research\/morphology-driven-deep-watershed-transform-for-3d-tooth-segmentation\/","og_site_name":"Centre for Computational Personalized Medicine","article_publisher":"https:\/\/www.facebook.com\/sano.science\/","article_modified_time":"2026-07-15T09:49:14+00:00","twitter_card":"summary_large_image","twitter_site":"@sanoscience","twitter_misc":{"Est. reading time":"1 minute"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/sano.science\/research\/morphology-driven-deep-watershed-transform-for-3d-tooth-segmentation\/","url":"https:\/\/sano.science\/research\/morphology-driven-deep-watershed-transform-for-3d-tooth-segmentation\/","name":"Morphology-Driven Deep Watershed Transform for 3D Tooth Segmentation - Centre for Computational Personalized Medicine","isPartOf":{"@id":"https:\/\/sano.science\/#website"},"datePublished":"2026-07-15T09:49:06+00:00","dateModified":"2026-07-15T09:49:14+00:00","breadcrumb":{"@id":"https:\/\/sano.science\/research\/morphology-driven-deep-watershed-transform-for-3d-tooth-segmentation\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/sano.science\/research\/morphology-driven-deep-watershed-transform-for-3d-tooth-segmentation\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/sano.science\/research\/morphology-driven-deep-watershed-transform-for-3d-tooth-segmentation\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/sano.science\/"},{"@type":"ListItem","position":2,"name":"Research","item":"https:\/\/sano.science\/research\/"},{"@type":"ListItem","position":3,"name":"Publications","item":"https:\/\/sano.science\/research-type\/publications\/"},{"@type":"ListItem","position":4,"name":"Morphology-Driven Deep Watershed Transform for 3D Tooth Segmentation"}]},{"@type":"WebSite","@id":"https:\/\/sano.science\/#website","url":"https:\/\/sano.science\/","name":"Centre for Computational Personalized Medicine","description":"Sano \u2013 Centre for Computational Medicine","publisher":{"@id":"https:\/\/sano.science\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/sano.science\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/sano.science\/#organization","name":"Sano \u2013 Centre for Computational Medicine","alternateName":"Sano","url":"https:\/\/sano.science\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/sano.science\/#\/schema\/logo\/image\/","url":"https:\/\/sano.science\/wp-content\/uploads\/2024\/05\/logo_sano_podstawowe.png","contentUrl":"https:\/\/sano.science\/wp-content\/uploads\/2024\/05\/logo_sano_podstawowe.png","width":700,"height":265,"caption":"Sano \u2013 Centre for Computational Medicine"},"image":{"@id":"https:\/\/sano.science\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/sano.science\/","https:\/\/x.com\/sanoscience","https:\/\/www.linkedin.com\/company\/sanoscience\/","https:\/\/www.youtube.com\/channel\/UCDZ_8TcjMWUG2ZcgKKgfpwQ","https:\/\/bsky.app\/profile\/sanoscience.bsky.social"]}]}},"acf":[],"gutenberg_blocks":[{"blockName":"custom-styles","attrs":{"styles":""}},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-4kWuNl","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Accurate segmentation of dentomaxillofacial structures in Cone-Beam Computed Tomography (CBCT) scans poses significant technical challenges, particularly for fine anatomical details such as root apices and nerve canals. Precise delineation of these structures is essential for assessing root resorption and supporting surgical planning in digital dentistry.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Accurate segmentation of dentomaxillofacial structures in Cone-Beam Computed Tomography (CBCT) scans poses significant technical challenges, particularly for fine anatomical details such as root apices and nerve canals. Precise delineation of these structures is essential for assessing root resorption and supporting surgical planning in digital dentistry.<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-we4yR1","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">This work presents a method developed in the scope of the ToothFairy3 Challenge, combining instance detection and multi-class segmentation of dentomaxillofacial structures into a single unified framework. The approach adapts a Deep Watershed technique, representing each anatomical structure as a continuous 3D energy basin that encodes voxel distances to class boundaries. This instance-aware formulation is particularly well-suited to handling the narrow, geometrically complex structures that make this segmentation task demanding.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">This work presents a method developed in the scope of the ToothFairy3 Challenge, combining instance detection and multi-class segmentation of dentomaxillofacial structures into a single unified framework. The approach adapts a Deep Watershed technique, representing each anatomical structure as a continuous 3D energy basin that encodes voxel distances to class boundaries. This instance-aware formulation is particularly well-suited to handling the narrow, geometrically complex structures that make this segmentation task demanding.<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-hp5qSn","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">The method was trained and evaluated on the ToothFairy3 dataset, comprising 532 CBCT scans with voxel-level annotations, achieving a mean Dice coefficient of 0.742 and HD95 of 111.13 on the test set. The implementation is publicly available.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">The method was trained and evaluated on the ToothFairy3 dataset, comprising 532 CBCT scans with voxel-level annotations, achieving a mean Dice coefficient of 0.742 and HD95 of 111.13 on the test set. The implementation is publicly available.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-yK3wlB","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-hp5qSn","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Autors<\/strong>: Tomasz Szczepa\u0144ski, Szymon P\u0142otka<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Autors<\/strong>: Tomasz Szczepa\u0144ski, Szymon P\u0142otka<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-Foh52t","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:10px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"Read the article","button_type":"link","url":"https:\/\/openreview.net\/pdf\/b44be6adb46f5df93fe0acda06485b0aa7d47056.pdf","button_style":"primary","target":"_blank","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/31940","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":7,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/31940\/revisions"}],"predecessor-version":[{"id":31947,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/31940\/revisions\/31947"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=31940"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=31940"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=31940"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}