{"conceptdoi":"10.14278/rodare.4198","conceptrecid":"4198","created":"2026-01-07T14:04:14.863419+00:00","doi":"10.14278/rodare.4199","files":[{"bucket":"9254b7e9-bc97-4a75-a329-98456cfcaa0f","checksum":"md5:080c71677fa8fb76699c0fe0fa7b6055","key":"pyMarAI-1.0.0-ecat.zip","links":{"self":"https://rodare.hzdr.de/api/files/9254b7e9-bc97-4a75-a329-98456cfcaa0f/pyMarAI-1.0.0-ecat.zip"},"size":771346916,"type":"zip"},{"bucket":"9254b7e9-bc97-4a75-a329-98456cfcaa0f","checksum":"md5:fb5bbfab30e23c77542a2fd1eb82132e","key":"pyMarAI-1.0.0-nifti.zip","links":{"self":"https://rodare.hzdr.de/api/files/9254b7e9-bc97-4a75-a329-98456cfcaa0f/pyMarAI-1.0.0-nifti.zip"},"size":771346936,"type":"zip"}],"id":4199,"links":{"badge":"https://rodare.hzdr.de/badge/doi/10.14278/rodare.4199.svg","bucket":"https://rodare.hzdr.de/api/files/9254b7e9-bc97-4a75-a329-98456cfcaa0f","conceptbadge":"https://rodare.hzdr.de/badge/doi/10.14278/rodare.4198.svg","conceptdoi":"https://doi.org/10.14278/rodare.4198","doi":"https://doi.org/10.14278/rodare.4199","html":"https://rodare.hzdr.de/record/4199","latest":"https://rodare.hzdr.de/api/records/4199","latest_html":"https://rodare.hzdr.de/record/4199","self":"https://rodare.hzdr.de/api/records/4199"},"metadata":{"access_right":"open","access_right_category":"success","communities":[{"id":"health"},{"id":"hzdr"},{"id":"pet-center"},{"id":"rodare"},{"id":"zrt"}],"creators":[{"name":"Maus, Jens","orcid":"0000-0002-7195-9927"},{"name":"Nitschke, Janina"},{"name":"Nikulin, Pavel","orcid":"0000-0002-4568-4018"},{"name":"Hofheinz, Frank","orcid":"0000-0001-8016-4643"},{"name":"Barth, Mareike"},{"name":"Lemm, Sandy","orcid":"0000-0001-6763-5957"},{"name":"Richter, Lena"},{"name":"Pietzsch, Jens","orcid":"0000-0002-1610-1493"},{"name":"Braune, Anja","orcid":"0000-0001-7707-9413"},{"name":"Ullrich, Martin","orcid":"0000-0001-6104-6676"}],"description":"<p>Collection of neural network models for automatic image segmentation of microscopic tumor spheroids. Intended to be used with nnU-Net deep-learning framework. Trained and tested on a total of microscopic images of mouse pheochromocytoma (MPC) tumor cells.</p>\n\n<p>In addition to the trained network model, a PyQt5-based graphical user interface tool is provided. This tool provides a complete pipeline for handling microscopic spheroid image data, running deep-learning&ndash;based delineation, and curating results for continuous model improvement.</p>\n\n<p>For installation and usage instructions, please visit <a href=\"https://github.com/hzdr-MedImaging/pyMarAI\">https://github.com/hzdr-MedImaging/pyMarAI</a></p>\n\n<p>Please cite&nbsp;<a href=\"https://www.nature.com/articles/s41592-020-01008-z\">nnU-Net</a>&nbsp;and the respective paper when using pyMarAI.</p>\n\n<p>List of available model types:</p>\n\n<ul>\n\t<li><code>pyMarAI-1.0.0-ecat.zip</code>: nnUNetv2 ready network (for ECAT7)</li>\n\t<li><code>pyMarAI-1.0.0-nifti.zip</code>: nnUNetv2 ready network (for NIFTI)</li>\n</ul>","doc_id":"1","doi":"10.14278/rodare.4199","keywords":["Tumor Spheroid Imaging","Radiopharmacological Treatment Response Assays","Delineation","Cancer","Deep-Learning","Artifical Intelligence","Convolutional Neural Networks","Network model"],"license":{"id":"CC-BY-SA-4.0"},"pub_id":"42498","publication_date":"2026-01-07","related_identifiers":[{"identifier":"https://www.hzdr.de/publications/Publ-42498","relation":"isIdenticalTo","scheme":"url"},{"identifier":"https://www.hzdr.de/publications/Publ-42497","relation":"isReferencedBy","scheme":"url"},{"identifier":"https://github.com/hzdr-MedImaging/pyMarAI","relation":"isSupplementTo","scheme":"url"},{"identifier":"10.14278/rodare.4198","relation":"isVersionOf","scheme":"doi"}],"relations":{"version":[{"count":1,"index":0,"is_last":true,"last_child":{"pid_type":"recid","pid_value":"4199"},"parent":{"pid_type":"recid","pid_value":"4198"}}]},"resource_type":{"title":"Software","type":"software"},"title":"pyMarAI: nnU-Net-based Tumor Spheroids Auto Delineation","version":"1.0.0"},"owners":[1060],"revision":13,"stats":{"downloads":31.0,"unique_downloads":29.0,"unique_views":588.0,"version_downloads":31.0,"version_unique_downloads":29.0,"version_unique_views":588.0,"version_views":613.0,"version_volume":23911754736.0,"views":613.0,"volume":23911754736.0},"updated":"2026-01-07T15:21:39.562503+00:00"}