arXiv:2609.29779<\/a> \u00b7 Code: github.com\/SanoScience\/mind-the-gap<\/p>\n\n\n\n<\/p>\n","protected":false},"excerpt":{"rendered":"
Gniewosz Drwi\u0119ga, Postdoc, Medical Image Analysis Team, Sano Centre for Computational Medicine, Krakow, PL<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-32151","seminars","type-seminars","status-publish","hentry"],"yoast_head":"\n
203. Mind the Gap: Mesh-Guided Repair of Broken Vessels - 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\/seminars\/203-mind-the-gap-mesh-guided-repair-of-broken-vessels\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"203. Mind the Gap: Mesh-Guided Repair of Broken Vessels\" \/>\n<meta property=\"og:description\" content=\"Gniewosz Drwi\u0119ga, Postdoc, Medical Image Analysis Team, Sano Centre for Computational Medicine, Krakow, PL\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/seminars\/203-mind-the-gap-mesh-guided-repair-of-broken-vessels\/\" \/>\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=\"2026-10-23T10:23:24+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=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/203-mind-the-gap-mesh-guided-repair-of-broken-vessels\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/seminars\\\/203-mind-the-gap-mesh-guided-repair-of-broken-vessels\\\/\",\"name\":\"203. Mind the Gap: Mesh-Guided Repair of Broken Vessels - Centre for Computational Personalized Medicine\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/sano.science\\\/#website\"},\"datePublished\":\"2026-07-21T10:09:02+00:00\",\"dateModified\":\"2026-10-23T10:23:24+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/203-mind-the-gap-mesh-guided-repair-of-broken-vessels\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/sano.science\\\/seminars\\\/203-mind-the-gap-mesh-guided-repair-of-broken-vessels\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/sano.science\\\/seminars\\\/203-mind-the-gap-mesh-guided-repair-of-broken-vessels\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/sano.science\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Seminars\",\"item\":\"https:\\\/\\\/sano.science\\\/seminars\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"203. Mind the Gap: Mesh-Guided Repair of Broken Vessels\"}]},{\"@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\"]}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"203. Mind the Gap: Mesh-Guided Repair of Broken Vessels - 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\/seminars\/203-mind-the-gap-mesh-guided-repair-of-broken-vessels\/","og_locale":"en_US","og_type":"article","og_title":"203. Mind the Gap: Mesh-Guided Repair of Broken Vessels","og_description":"Gniewosz Drwi\u0119ga, Postdoc, Medical Image Analysis Team, Sano Centre for Computational Medicine, Krakow, PL","og_url":"https:\/\/sano.science\/seminars\/203-mind-the-gap-mesh-guided-repair-of-broken-vessels\/","og_site_name":"Centre for Computational Personalized Medicine","article_publisher":"https:\/\/www.facebook.com\/sano.science\/","article_modified_time":"2026-10-23T10:23:24+00:00","twitter_card":"summary_large_image","twitter_site":"@sanoscience","twitter_misc":{"Est. reading time":"2 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/sano.science\/seminars\/203-mind-the-gap-mesh-guided-repair-of-broken-vessels\/","url":"https:\/\/sano.science\/seminars\/203-mind-the-gap-mesh-guided-repair-of-broken-vessels\/","name":"203. Mind the Gap: Mesh-Guided Repair of Broken Vessels - Centre for Computational Personalized Medicine","isPartOf":{"@id":"https:\/\/sano.science\/#website"},"datePublished":"2026-07-21T10:09:02+00:00","dateModified":"2026-10-23T10:23:24+00:00","breadcrumb":{"@id":"https:\/\/sano.science\/seminars\/203-mind-the-gap-mesh-guided-repair-of-broken-vessels\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/sano.science\/seminars\/203-mind-the-gap-mesh-guided-repair-of-broken-vessels\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/sano.science\/seminars\/203-mind-the-gap-mesh-guided-repair-of-broken-vessels\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/sano.science\/"},{"@type":"ListItem","position":2,"name":"Seminars","item":"https:\/\/sano.science\/seminars\/"},{"@type":"ListItem","position":3,"name":"203. Mind the Gap: Mesh-Guided Repair of Broken Vessels"}]},{"@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\/heading","attrs":{"anchor":"h-abstract","epAnimationGeneratedClass":"edplus_anim-n92Tj8","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h2 id=\"h-abstract\" class=\"wp-block-heading eplus-wrapper\">Abstract:<\/h2>\n","innerContent":["\n<h2 id=\"h-abstract\" class=\"wp-block-heading eplus-wrapper\">Abstract:<\/h2>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-IAeoPH","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Vessel segmentation is usually trained and evaluated voxel by voxel, yet a missing bridge of a few voxels can split a vessel into pieces while barely changing Dice, breaking downstream centreline extraction, branch labelling and flow simulation. We present Mind the Gap, a post-processing framework that repairs the connectivity of an existing nnU-Net vessel mask without retraining and without image intensities or probability maps. For each predicted mask, an anatomy-specific template mesh (tube, torus or sphere) is fitted to the mask surface by a four-stage graph-convolutional decoder, optimised per case with a Chamfer loss and geometric regularisers. The mesh is never voxelised as the output: it only proposes or validates thin bridges between disconnected components, each accepted only if it merges components within a strict foreground-growth limit. On out-of-fold nnU-Net predictions for four public datasets covering three anatomies, SEGA and AortaSeg24 (aorta, n = 145), TopCoW (Circle of Willis, n = 125) and PARSE (pulmonary arteries, n = 100), Dice changed by at most 0.003, whereas connected-component Dice rose from 0.60 to 0.99, 0.72 to 0.84 and 0.03 to 0.86, respectively, and the mean number of components fell to 1.0\u20131.6. The main cost was in the Circle of Willis, where the false-branch fraction rose from 0.040 to 0.063 and circles disconnected in the reference were usually connected. A first-order meta-learned (FOMAML) initialisation reaches the fit quality of 500 standard steps within 178\u2013280 steps, bringing fitting and repair to about 30 s per aortic case. Explicit meshes can thus provide a practical geometric prior for repairing vascular topology.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Vessel segmentation is usually trained and evaluated voxel by voxel, yet a missing bridge of a few voxels can split a vessel into pieces while barely changing Dice, breaking downstream centreline extraction, branch labelling and flow simulation. We present Mind the Gap, a post-processing framework that repairs the connectivity of an existing nnU-Net vessel mask without retraining and without image intensities or probability maps. For each predicted mask, an anatomy-specific template mesh (tube, torus or sphere) is fitted to the mask surface by a four-stage graph-convolutional decoder, optimised per case with a Chamfer loss and geometric regularisers. The mesh is never voxelised as the output: it only proposes or validates thin bridges between disconnected components, each accepted only if it merges components within a strict foreground-growth limit. On out-of-fold nnU-Net predictions for four public datasets covering three anatomies, SEGA and AortaSeg24 (aorta, n = 145), TopCoW (Circle of Willis, n = 125) and PARSE (pulmonary arteries, n = 100), Dice changed by at most 0.003, whereas connected-component Dice rose from 0.60 to 0.99, 0.72 to 0.84 and 0.03 to 0.86, respectively, and the mean number of components fell to 1.0\u20131.6. The main cost was in the Circle of Willis, where the false-branch fraction rose from 0.040 to 0.063 and circles disconnected in the reference were usually connected. A first-order meta-learned (FOMAML) initialisation reaches the fit quality of 500 standard steps within 178\u2013280 steps, bringing fitting and repair to about 30 s per aortic case. Explicit meshes can thus provide a practical geometric prior for repairing vascular topology.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-FxqttU","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-2YspjC","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><a href=\"https:\/\/sano.science\/people\/gniewosz-drwiega\/\" data-type=\"people\" data-id=\"28666\">Gniewosz Drwi\u0119ga<\/a><sup>1<\/sup>,\u00a0<a href=\"https:\/\/sano.science\/people\/wojciech-szymanski\/\" data-type=\"people\" data-id=\"25066\">Wojciech Szyma\u0144ski<\/a><sup>1,2<\/sup>,\u00a0<a href=\"https:\/\/sano.science\/people\/marek-wodzinski\/\" data-type=\"people\" data-id=\"26360\">Marek Wodzi\u0144ski<\/a><sup>1,2<\/sup><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><a href=\"https:\/\/sano.science\/people\/gniewosz-drwiega\/\" data-type=\"people\" data-id=\"28666\">Gniewosz Drwi\u0119ga<\/a><sup>1<\/sup>,\u00a0<a href=\"https:\/\/sano.science\/people\/wojciech-szymanski\/\" data-type=\"people\" data-id=\"25066\">Wojciech Szyma\u0144ski<\/a><sup>1,2<\/sup>,\u00a0<a href=\"https:\/\/sano.science\/people\/marek-wodzinski\/\" data-type=\"people\" data-id=\"26360\">Marek Wodzi\u0144ski<\/a><sup>1,2<\/sup><\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-NHZnwb","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><sup>1<\/sup> Sano \u2013 Centre for Computational Personalised Medicine International Research Foundation, Krak\u00f3w, Poland<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><sup>1<\/sup> Sano \u2013 Centre for Computational Personalised Medicine International Research Foundation, Krak\u00f3w, Poland<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-Qr9w2G","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><sup>2<\/sup> AGH University of Krakow, Poland<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><sup>2<\/sup> AGH University of Krakow, Poland<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-1ovBh1","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><em>ShapeMI 2026 @ MICCAI \u00b7 Best Poster Award<\/em><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><em>ShapeMI 2026 @ MICCAI \u00b7 Best Poster Award<\/em><\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-FxqttU","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-lIk39J","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Keywords: <\/strong>vessel segmentation \u00b7 topology \u00b7 mesh deformation \u00b7 geometric deep learning \u00b7 nnU-Net<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Keywords: <\/strong>vessel segmentation \u00b7 topology \u00b7 mesh deformation \u00b7 geometric deep learning \u00b7 nnU-Net<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"30px","epAnimationGeneratedClass":"edplus_anim-FxqttU","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/heading","attrs":{"anchor":"h-about-the-author","epAnimationGeneratedClass":"edplus_anim-U9uDsB","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<h2 id=\"h-about-the-author\" class=\"wp-block-heading eplus-wrapper\">About the author:<\/h2>\n","innerContent":["\n<h2 id=\"h-about-the-author\" class=\"wp-block-heading eplus-wrapper\">About the author:<\/h2>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-4z0Tm1","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Gniewosz Drwi\u0119ga holds a PhD in Systems Neurobiology from Jagiellonian University, where he also worked as an Assistant Professor in the Department of Computational Biophysics and Bioinformatics. His scientific interests include computer vision, medical image analysis, and deep learning.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Gniewosz Drwi\u0119ga holds a PhD in Systems Neurobiology from Jagiellonian University, where he also worked as an Assistant Professor in the Department of Computational Biophysics and Bioinformatics. His scientific interests include computer vision, medical image analysis, and deep learning.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"10px","epAnimationGeneratedClass":"edplus_anim-FxqttU","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-0brWOn","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Preprint: <a href=\"arXiv:2609.29779\">arXiv:2609.29779<\/a> \u00b7 Code: github.com\/SanoScience\/mind-the-gap<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Preprint: <a href=\"arXiv:2609.29779\">arXiv:2609.29779<\/a> \u00b7 Code: github.com\/SanoScience\/mind-the-gap<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-SK8uxK","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><\/p>\n"]}],"meta_data":{"event_day":"2026-10-26","event_time":"14.00 CET","event_guest":"","has_medias":true,"medias":[{"icon":{"ID":1144,"id":1144,"title":"clock","filename":"clock.svg","filesize":1479,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","link":"https:\/\/sano.science\/seminars\/79-digital-behaviour-change-interventions-dbci-from-design-to-implementation\/clock\/","alt":"clock Sano Seminar","author":"7","description":"","caption":"Sano Seminar clock","name":"clock","status":"inherit","uploaded_to":13471,"date":"2023-06-01 13:24:42","modified":"2024-10-09 16:41:04","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":56,"height":57,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","thumbnail-width":147,"thumbnail-height":150,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","medium-width":294,"medium-height":300,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","medium_large-width":768,"medium_large-height":783,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","large-width":1004,"large-height":1024,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","1536x1536-width":56,"1536x1536-height":57,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/clock.svg","2048x2048-width":56,"2048x2048-height":57}},"title":"26th October 2026, 14.00 CEST","link":""},{"icon":{"ID":1146,"id":1146,"title":"camera","filename":"camera.svg","filesize":1129,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","link":"https:\/\/sano.science\/seminars\/79-digital-behaviour-change-interventions-dbci-from-design-to-implementation\/camera\/","alt":"camera Sano Seminar","author":"7","description":"","caption":"Sano Seminar camera","name":"camera","status":"inherit","uploaded_to":13471,"date":"2023-06-01 13:25:24","modified":"2024-10-09 16:42:29","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":60,"height":38,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","thumbnail-width":150,"thumbnail-height":95,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","medium-width":300,"medium-height":190,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","medium_large-width":768,"medium_large-height":486,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","large-width":1024,"large-height":648,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","1536x1536-width":60,"1536x1536-height":38,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/camera.svg","2048x2048-width":60,"2048x2048-height":38}},"title":"Join via ZOOM on","link":{"title":"seminar.sano.science","url":"https:\/\/us06web.zoom.us\/j\/81263292238#success","target":"_blank"}}]},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars\/32151","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/seminars"}],"version-history":[{"count":13,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars\/32151\/revisions"}],"predecessor-version":[{"id":33057,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/seminars\/32151\/revisions\/33057"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=32151"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}