{"id":44381,"date":"2021-09-02T15:14:32","date_gmt":"2021-09-02T13:14:32","guid":{"rendered":"https:\/\/www.h-its.org\/de\/?p=44381"},"modified":"2021-09-02T15:14:33","modified_gmt":"2021-09-02T13:14:33","slug":"biomedisa","status":"publish","type":"post","link":"https:\/\/www.h-its.org\/de\/2021\/09\/02\/biomedisa\/","title":{"rendered":"Biomedisa – Open-Source Online-Anwendung f\u00fcr die Segmentierung biomedizinischer Aufnahmen"},"content":{"rendered":"\n

In vielen wissenschaftlichen Disziplinen liegt der Schl\u00fcssel zu einem umfassenden Verst\u00e4ndnis dreidimensionaler Aufnahmen \u2013 wie sie zum Beispiel im Computertomographen oder in der Lichtmikroskopie entstehen \u2013 in der Segmentierung. Dabei handelt es sich um eine langwierige und sehr zeitaufw\u00e4ndige T\u00e4tigkeit, die zudem \u00e4u\u00dferst fehleranf\u00e4llig ist, wenn sie von Hand ausgef\u00fchrt wird.<\/p>\n\n\n\n

Die Biomedical Image Segmentation App \u201eBiomedisa\u201c ist eine frei verf\u00fcgbare und leicht zu bedienende Open-Source Online-Anwendung, die speziell f\u00fcr die halbautomatische Segmentierung entwickelt wurde. Die Segmentierung basiert dabei auf einer intelligenten Interpolation einiger weniger vorsegmentierter Schichten, wobei die zugrundeliegenden Bilddaten vollst\u00e4ndig mit einbezogen werden.<\/p>\n\n\n\n

Das semi-automatische Verfahren eignet sich insbesondere f\u00fcr 3D-Datens\u00e4tze einzelner Proben unbekannter Morphologie oder zur Erstellung von Trainingsdaten f\u00fcr eine automatisierte Segmentierung von Probenserien. Hierf\u00fcr erm\u00f6glicht \u201eBiomedisa\u201c das Training sogenannter Convolutional Neural Networks (CNNs), um eine weitgehend automatische Segmentierung vieler Proben mit \u00e4hnlicher Morphologie zu erm\u00f6glichen.<\/p>\n\n\n\n

Die Plattform ist \u00fcber einen Webbrowser zug\u00e4nglich (https:\/\/biomedisa.org<\/a>), eine komplexe und langwierige Konfiguration der Software oder der Modellparameter ist nicht erforderlich. Die One-Button-L\u00f6sung kann bei verschiedenen 3D-Bildgebungsverfahren und zahlreichen biomedizinischen Anwendungen eingesetzt werden.<\/p>\n\n\n\n

\u201eBiomedisa\u201c wurde in der Data Mining an Uncertainty Quantification (DMQ)<\/a> Gruppe am HITS entwickelt.<\/p>\n\n\n\n

Weitere Quellen:<\/strong><\/p>\n\n\n\n

Philipp D. L\u00f6sel, Thomas van de Kamp, Alejandra Jayme, Alexey Ershov, Tom\u00e1\u0161 Farag\u00f3, Olaf Pichler, Nicholas Tan Jerome, Narendar Aadepu, Sabine Bremer, Suren A. Chilingaryan, Michael Heethoff, Andreas Kopmann, Janes Odar, Sebastian Schmelzle, Marcus Zuber, Joachim Wittbrodt, Tilo Baumbach & Vincent Heuveline: Introducing Biomedisa as an open-source online platform for biomedical image segmentation. Nature Communications, 4 November 2020. DOI 10.1038\/s41467-020-19303-w<\/a><\/em><\/p>\n\n\n\n

Tutorials:<\/strong><\/p>\n\n\n\n

Hier geht es zum Biomedisa YouTube Kanal.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

In vielen wissenschaftlichen Disziplinen liegt der Schl\u00fcssel zu einem umfassenden Verst\u00e4ndnis dreidimensionaler Aufnahmen \u2013 wie sie zum Beispiel im Computertomographen …<\/p>\n","protected":false},"author":6,"featured_media":43425,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[1421],"hits-research-group":[],"class_list":["post-44381","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-software-news"],"acf":[],"yoast_head":"\nBiomedisa - Open-Source Online-Anwendung f\u00fcr die Segmentierung biomedizinischer Aufnahmen - HITS gGmbH<\/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:\/\/www.h-its.org\/de\/2021\/09\/02\/biomedisa\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Biomedisa - 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