{"version":"1.0","provider_name":"German Conference on Medical Image Computing","provider_url":"https:\/\/www.bvm-conf.org\/de\/","title":"Poster 49 &#8212; German Conference on Medical Image Computing","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"CFZK6nnUTZ\"><a href=\"https:\/\/www.bvm-conf.org\/de\/bvm2021\/postersession\/poster49\/\">Poster 49<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/www.bvm-conf.org\/de\/bvm2021\/postersession\/poster49\/embed\/#?secret=CFZK6nnUTZ\" width=\"600\" height=\"338\" title=\"&#8222;Poster 49&#8220; &#8211; German Conference on Medical Image Computing\" data-secret=\"CFZK6nnUTZ\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/www.bvm-conf.org\/wp-includes\/js\/wp-embed.min.js\n<\/script>\n","description":"Deep Learning-basierte Oberfl\u00e4chenrekonstruktion aus Bin\u00e4rmasken Carina Tschigor, Grzegorz Chlebus, Christian Schumann Fraunhofer MEVIS, Bremen Abstract Die Darstellung anatomischer Strukturen auf Basis von Segmentierungsergebnissen in Form von Bin\u00e4rmasken ist eine grundlegende Aufgabe im Bereich der medizinischen Visualisierung. Hierf\u00fcr werden meist polygonale Oberfl\u00e4chen genutzt. Bei Bin\u00e4rmasken fehlt jedoch die Information \u00fcber die tats\u00e4chliche Oberfl\u00e4che, wodurch die erzeugten&hellip;","thumbnail_url":"https:\/\/www.bvm-conf.org\/wp-content\/uploads\/2020\/09\/cropped-BVM-Logo.png","thumbnail_width":964,"thumbnail_height":964}