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pyMarAI: nnU-Net-based Tumor Spheroids Auto Delineation

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

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    <dct:title>pyMarAI: nnU-Net-based Tumor Spheroids Auto Delineation</dct:title>
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    <dcat:keyword>Tumor Spheroid Imaging</dcat:keyword>
    <dcat:keyword>Radiopharmacological Treatment Response Assays</dcat:keyword>
    <dcat:keyword>Delineation</dcat:keyword>
    <dcat:keyword>Cancer</dcat:keyword>
    <dcat:keyword>Deep-Learning</dcat:keyword>
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    <dct:description>&lt;p&gt;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.&lt;/p&gt; &lt;p&gt;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&amp;ndash;based delineation, and curating results for continuous model improvement.&lt;/p&gt; &lt;p&gt;For installation and usage instructions, please visit &lt;a href="https://github.com/hzdr-MedImaging/pyMarAI"&gt;https://github.com/hzdr-MedImaging/pyMarAI&lt;/a&gt;&lt;/p&gt; &lt;p&gt;Please cite&amp;nbsp;&lt;a href="https://www.nature.com/articles/s41592-020-01008-z"&gt;nnU-Net&lt;/a&gt;&amp;nbsp;and the respective paper when using pyMarAI.&lt;/p&gt; &lt;p&gt;List of available model types:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;&lt;code&gt;pyMarAI-1.0.0-ecat.zip&lt;/code&gt;: nnUNetv2 ready network (for ECAT7)&lt;/li&gt; &lt;li&gt;&lt;code&gt;pyMarAI-1.0.0-nifti.zip&lt;/code&gt;: nnUNetv2 ready network (for NIFTI)&lt;/li&gt; &lt;/ul&gt;</dct:description>
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Publication date:
January 7, 2026
DOI:
10.14278/rodare.4199

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Keyword(s):
Tumor Spheroid Imaging Radiopharmacological Treatment Response Assays Delineation Cancer Deep-Learning Artifical Intelligence Convolutional Neural Networks Network model
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