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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  <identifier identifierType="DOI">10.14278/rodare.4199</identifier>
  <creators>
    <creator>
      <creatorName>Maus, Jens</creatorName>
      <givenName>Jens</givenName>
      <familyName>Maus</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-7195-9927</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Nitschke, Janina</creatorName>
      <givenName>Janina</givenName>
      <familyName>Nitschke</familyName>
    </creator>
    <creator>
      <creatorName>Nikulin, Pavel</creatorName>
      <givenName>Pavel</givenName>
      <familyName>Nikulin</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-4568-4018</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Hofheinz, Frank</creatorName>
      <givenName>Frank</givenName>
      <familyName>Hofheinz</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-8016-4643</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Barth, Mareike</creatorName>
      <givenName>Mareike</givenName>
      <familyName>Barth</familyName>
    </creator>
    <creator>
      <creatorName>Lemm, Sandy</creatorName>
      <givenName>Sandy</givenName>
      <familyName>Lemm</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6763-5957</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Richter, Lena</creatorName>
      <givenName>Lena</givenName>
      <familyName>Richter</familyName>
    </creator>
    <creator>
      <creatorName>Pietzsch, Jens</creatorName>
      <givenName>Jens</givenName>
      <familyName>Pietzsch</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-1610-1493</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Braune, Anja</creatorName>
      <givenName>Anja</givenName>
      <familyName>Braune</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-7707-9413</nameIdentifier>
    </creator>
    <creator>
      <creatorName>Ullrich, Martin</creatorName>
      <givenName>Martin</givenName>
      <familyName>Ullrich</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6104-6676</nameIdentifier>
    </creator>
  </creators>
  <titles>
    <title>pyMarAI: nnU-Net-based Tumor Spheroids Auto Delineation</title>
  </titles>
  <publisher>Rodare</publisher>
  <publicationYear>2026</publicationYear>
  <subjects>
    <subject>Tumor Spheroid Imaging</subject>
    <subject>Radiopharmacological Treatment Response Assays</subject>
    <subject>Delineation</subject>
    <subject>Cancer</subject>
    <subject>Deep-Learning</subject>
    <subject>Artifical Intelligence</subject>
    <subject>Convolutional Neural Networks</subject>
    <subject>Network model</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2026-01-07</date>
  </dates>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/4199</alternateIdentifier>
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  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo">https://www.hzdr.de/publications/Publ-42498</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsReferencedBy">https://www.hzdr.de/publications/Publ-42497</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsSupplementTo">https://github.com/hzdr-MedImaging/pyMarAI</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.14278/rodare.4198</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/health</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/hzdr</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/pet-center</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/rodare</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/zrt</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by-sa/4.0/legalcode">Creative Commons Attribution Share Alike 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&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;</description>
  </descriptions>
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