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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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Maus, Jens</dc:creator>
  <dc:creator>Nitschke, Janina</dc:creator>
  <dc:creator>Nikulin, Pavel</dc:creator>
  <dc:creator>Hofheinz, Frank</dc:creator>
  <dc:creator>Barth, Mareike</dc:creator>
  <dc:creator>Lemm, Sandy</dc:creator>
  <dc:creator>Richter, Lena</dc:creator>
  <dc:creator>Pietzsch, Jens</dc:creator>
  <dc:creator>Braune, Anja</dc:creator>
  <dc:creator>Ullrich, Martin</dc:creator>
  <dc:date>2026-01-07</dc:date>
  <dc:description>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.

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.

For installation and usage instructions, please visit https://github.com/hzdr-MedImaging/pyMarAI

Please cite nnU-Net and the respective paper when using pyMarAI.

List of available model types:


	pyMarAI-1.0.0-ecat.zip: nnUNetv2 ready network (for ECAT7)
	pyMarAI-1.0.0-nifti.zip: nnUNetv2 ready network (for NIFTI)
</dc:description>
  <dc:identifier>https://rodare.hzdr.de/record/4199</dc:identifier>
  <dc:identifier>10.14278/rodare.4199</dc:identifier>
  <dc:identifier>oai:rodare.hzdr.de:4199</dc:identifier>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-42498</dc:relation>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-42497</dc:relation>
  <dc:relation>url:https://github.com/hzdr-MedImaging/pyMarAI</dc:relation>
  <dc:relation>doi:10.14278/rodare.4198</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/health</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/hzdr</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/pet-center</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/rodare</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/zrt</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-sa/4.0/legalcode</dc:rights>
  <dc:subject>Tumor Spheroid Imaging</dc:subject>
  <dc:subject>Radiopharmacological Treatment Response Assays</dc:subject>
  <dc:subject>Delineation</dc:subject>
  <dc:subject>Cancer</dc:subject>
  <dc:subject>Deep-Learning</dc:subject>
  <dc:subject>Artifical Intelligence</dc:subject>
  <dc:subject>Convolutional Neural Networks</dc:subject>
  <dc:subject>Network model</dc:subject>
  <dc:title>pyMarAI: nnU-Net-based Tumor Spheroids Auto Delineation</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>software</dc:type>
</oai_dc:dc>