Software Open Access
Maus, Jens;
Nitschke, Janina;
Nikulin, Pavel;
Hofheinz, Frank;
Barth, Mareike;
Lemm, Sandy;
Richter, Lena;
Pietzsch, Jens;
Braune, Anja;
Ullrich, Martin
{
"type": "article",
"abstract": "<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>",
"version": "1.0.0",
"title": "pyMarAI: nnU-Net-based Tumor Spheroids Auto Delineation",
"author": [
{
"family": "Maus, Jens"
},
{
"family": "Nitschke, Janina"
},
{
"family": "Nikulin, Pavel"
},
{
"family": "Hofheinz, Frank"
},
{
"family": "Barth, Mareike"
},
{
"family": "Lemm, Sandy"
},
{
"family": "Richter, Lena"
},
{
"family": "Pietzsch, Jens"
},
{
"family": "Braune, Anja"
},
{
"family": "Ullrich, Martin"
}
],
"issued": {
"date-parts": [
[
2026,
1,
7
]
]
},
"publisher": "Rodare",
"id": "4199",
"DOI": "10.14278/rodare.4199"
}
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