January 7, 2026 Software Open Access

Maus, Jens; Nitschke, Janina; Nikulin, Pavel; Hofheinz, Frank; Barth, Mareike; Lemm, Sandy; Richter, Lena; Pietzsch, Jens; Braune, Anja; Ullrich, Martin

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    "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>", 
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    "title": "pyMarAI: nnU-Net-based Tumor Spheroids Auto Delineation", 
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