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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"title": "pyMarAI: nnU-Net-based Tumor Spheroids Auto Delineation",
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"Tumor Spheroid Imaging",
"Radiopharmacological Treatment Response Assays",
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"Cancer",
"Deep-Learning",
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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–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 <a href=\"https://www.nature.com/articles/s41592-020-01008-z\">nnU-Net</a> 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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| All versions | This version | |
|---|---|---|
| Views | 151 | 151 |
| Downloads | 3 | 3 |
| Data volume | 2.3 GB | 2.3 GB |
| Unique views | 146 | 146 |
| Unique downloads | 3 | 3 |