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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{
  "@type": "SoftwareSourceCode", 
  "identifier": "https://doi.org/10.14278/rodare.4199", 
  "creator": [
    {
      "@type": "Person", 
      "name": "Maus, Jens", 
      "@id": "https://orcid.org/0000-0002-7195-9927"
    }, 
    {
      "@type": "Person", 
      "name": "Nitschke, Janina"
    }, 
    {
      "@type": "Person", 
      "name": "Nikulin, Pavel", 
      "@id": "https://orcid.org/0000-0002-4568-4018"
    }, 
    {
      "@type": "Person", 
      "name": "Hofheinz, Frank", 
      "@id": "https://orcid.org/0000-0001-8016-4643"
    }, 
    {
      "@type": "Person", 
      "name": "Barth, Mareike"
    }, 
    {
      "@type": "Person", 
      "name": "Lemm, Sandy", 
      "@id": "https://orcid.org/0000-0001-6763-5957"
    }, 
    {
      "@type": "Person", 
      "name": "Richter, Lena"
    }, 
    {
      "@type": "Person", 
      "name": "Pietzsch, Jens", 
      "@id": "https://orcid.org/0000-0002-1610-1493"
    }, 
    {
      "@type": "Person", 
      "name": "Braune, Anja", 
      "@id": "https://orcid.org/0000-0001-7707-9413"
    }, 
    {
      "@type": "Person", 
      "name": "Ullrich, Martin", 
      "@id": "https://orcid.org/0000-0001-6104-6676"
    }
  ], 
  "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>", 
  "license": "https://creativecommons.org/licenses/by-sa/4.0/legalcode", 
  "keywords": [
    "Tumor Spheroid Imaging", 
    "Radiopharmacological Treatment Response Assays", 
    "Delineation", 
    "Cancer", 
    "Deep-Learning", 
    "Artifical Intelligence", 
    "Convolutional Neural Networks", 
    "Network model"
  ], 
  "codeRepository": "https://github.com/hzdr-MedImaging/pyMarAI", 
  "datePublished": "2026-01-07", 
  "@id": "https://doi.org/10.14278/rodare.4199", 
  "@context": "https://schema.org/", 
  "url": "https://rodare.hzdr.de/record/4199", 
  "sameAs": [
    "https://www.hzdr.de/publications/Publ-42498"
  ], 
  "version": "1.0.0", 
  "name": "pyMarAI: nnU-Net-based Tumor Spheroids Auto Delineation"
}