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Multi-channel Deep Transfer Learning for Nuclei Segmentation in Glioblastoma Cell Tissue Images

  • Thomas Wollmann
  • Julia Ivanova
  • Manuel Gunkel
  • Inn Chung
  • Holger Erfle
  • Karsten Rippe
  • Karl Rohr
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Segmentation and quantification of cell nuclei is an important task in tissue microscopy image analysis. We introduce a deep learning method leveraging atrous spatial pyramid pooling for cell segmentation. We also present two different approaches for transfer learning using datasets with a different number of channels. A quantitative comparison with previous methods was performed on challenging glioblastoma cell tissue images. We found that our transfer learning method improves the segmentation result.

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Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  • Thomas Wollmann
    • 1
  • Julia Ivanova
    • 1
  • Manuel Gunkel
    • 2
  • Inn Chung
    • 3
  • Holger Erfle
    • 2
  • Karsten Rippe
    • 3
  • Karl Rohr
    • 1
  1. 1.University of Heidelberg, BioQuant, IPMB, and DKFZ Heidelberg, Dept. Bioinformatics and Functional Genomics, Biomedical Computer Vision GroupHeidelbergDeutschland
  2. 2.High-Content Analysis of the Cell (HiCell) and Advanced Biological Screening Facility, BioQuant,University of HeidelbergHeidelbergDeutschland
  3. 3.Division of Chromatin Networks, DKFZ and BioQuantHeidelbergDeutschland

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