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  <doc>
    <id>3657</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>334</pageFirst>
    <pageLast>340</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>bookpart</type>
    <publisherName>Springer Vieweg</publisherName>
    <publisherPlace>Berlin, Heidelberg</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-11-05</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Variational Networks for Joint Image Reconstruction and Classification of Tumor Immune Cell Interactions in Melanoma Tissue Sections</title>
    <abstract language="eng">Immunotherapy is currently revolutionizing the treatment of cancer. Detailed analyses of tumor immune cell interaction in the tumor microenvironment will facilitate an accurate prediction of a patient’s clinical response. The automatic and reliable pre-screening of histological tissue sections for tumor infiltrating immune cells (TILs) will support the development of TIL-based predictive biomarkers for checkpoint immunotherapy. In this paper, a learning approach for image classification is presented, which allows various pattern inquires for different types of tissue section images. The underlying trainable reaction diffusion model combines classification and denoising. The model is trained using a stochastic generation of training data. The effectiveness of this approach is demonstrated for immunofluorescent and for Hematoxylin and Eosin (H&amp;E) stained melanoma section images. A particular focus is on the classification of TILs in the proximity to melanoma cells in an experimental melanoma mouse model and in human melanoma. This new learning approach for images of melanoma tissue sections will refine the strategy for the practical clinical application of biomarker research.</abstract>
    <parentTitle language="deu">Bildverarbeitung für die Medizin 2018. Informatik aktuell.</parentTitle>
    <identifier type="doi">10.1007/978-3-662-56537-7_86</identifier>
    <identifier type="issn">978-3-662-56537-7</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Alexander Effland</author>
    <submitter>Louisa Finke</submitter>
    <editor>Andreas Maier (et al.)</editor>
    <author>Michael Hölzel</author>
    <author>Teresa Klatzer</author>
    <author>Erich Kobler</author>
    <author>Jennifer Landsberg</author>
    <author>Leonie Neuhäuser</author>
    <author>Thomas Pock</author>
    <author>Martin Rumpf</author>
    <collection role="HertieResearch" number="">Publications PhD Researchers</collection>
  </doc>
  <doc>
    <id>3658</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>587</pageFirst>
    <pageLast>599</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>14</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-11-05</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Joint reconstruction and classification of tumor cells and cell interactions in melanoma tissue sections with synthesized training data</title>
    <abstract language="eng">Purpose&#13;
&#13;
Cancers are almost always diagnosed by morphologic features in tissue sections. In this context, machine learning tools provide new opportunities to describe tumor immune cell interactions within the tumor microenvironment and thus provide phenotypic information that might be predictive for the response to immunotherapy.&#13;
&#13;
Methods&#13;
&#13;
We develop a machine learning approach using variational networks for joint image denoising and classification of tissue sections for melanoma, which is an established model tumor for immuno-oncology research. The manual annotation of real training data would require substantial user interaction of experienced pathologists for each single training image, and the training of larger networks would rely on a very large number of such data sets with ground truth annotation. To overcome this bottleneck, we synthesize training data together with a proper tissue structure classification. To this end, a stochastic data generation process is used to mimic cell morphology, cell distribution and tissue architecture in the tumor microenvironment. Particular components of this tool are random placement and rotation of a large number of patches for presegmented cell nuclei, a stochastic fast marching approach to mimic the geometry of cells and texture generation based on a color covariance analysis of real data. Here, the generated training data reflect a large range of interaction patterns.&#13;
&#13;
Results&#13;
&#13;
In several applications to histological tissue sections, we analyze the efficiency and accuracy of the proposed approach. As a result, depending on the scenario considered, almost all cells and nuclei which ought to be detected are actually marked as classified and hardly any misclassifications occur.&#13;
&#13;
Conclusions&#13;
&#13;
The proposed method allows for a computer-aided screening of histological tissue sections utilizing variational networks with a particular emphasis on tumor immune cell interactions and on the robust cell nuclei classification.</abstract>
    <parentTitle language="eng">International Journal of Computer Assisted Radiology and Surgery</parentTitle>
    <identifier type="doi">10.1007/s11548-019-01919-z</identifier>
    <identifier type="issn">1861-6429</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Metadaten / metadata</licence>
    <author>Alexander Effland</author>
    <submitter>Louisa Finke</submitter>
    <author>Erich Kobler</author>
    <author>Anne Brandenburg</author>
    <author>Teresa Klatzer</author>
    <author>Leonie Neuhäuser</author>
    <author>Michael Hölzel</author>
    <author>Jennifer Landsberg</author>
    <author>Thomas Pock</author>
    <author>Martin Rumpf</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image reconstruction and classification</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Variational networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digital pathology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Tumor immune cell interaction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nuclei detection</value>
    </subject>
    <collection role="HertieResearch" number="">Publications PhD Researchers</collection>
    <thesisPublisher>Hertie School</thesisPublisher>
  </doc>
</export-example>
