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Automatic Detection of Tumor Buds in Pan-Cytokeratin Stained Colorectal Cancer Sections by a Hybrid Image Analysis Approach

  • Matthias BerglerEmail author
  • Michaela Benz
  • David Rauber
  • David Hartmann
  • Malte Kötter
  • Markus Eckstein
  • Regine Schneider-Stock
  • Arndt Hartmann
  • Susanne Merkel
  • Volker Bruns
  • Thomas Wittenberg
  • Carol Geppert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11435)

Abstract

This contribution introduces a novel approach to the automatic detection of tumor buds in a digitalized pan-cytokeratin stained colorectal cancer slide. Tumor buds are representing an invasive pattern and are frequently investigated as a new diagnostic factor for measuring the aggressiveness of colorectal cancer. However, counting the number of buds under the microscope in a high power field by eyeballing is a strenuous, lengthy and error-prone task, whereas an automated solution could save time for the pathologists and enhance reproducibility. We propose a new hybrid method that consists of two steps. First possible tumor bud candidates are detected using a chain of classical image processing methods. Afterwards a convolutional deep neural network is applied to filter and reduce the number of false positive candidates detected in the first step. By comparing the automatically detected buds with a gold standard created by manual annotations, we gain a score of 0.977 for precision and 0.934 for sensitivity in our test sets on over 8.000 tumor buds.

Keywords

Convolutional Neural Network (CNN) Medical image analysis Tumor budding Deep learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Matthias Bergler
    • 1
    Email author
  • Michaela Benz
    • 1
  • David Rauber
    • 1
  • David Hartmann
    • 1
  • Malte Kötter
    • 2
  • Markus Eckstein
    • 2
  • Regine Schneider-Stock
    • 2
  • Arndt Hartmann
    • 2
  • Susanne Merkel
    • 3
  • Volker Bruns
    • 1
  • Thomas Wittenberg
    • 1
  • Carol Geppert
    • 2
  1. 1.Fraunhofer Institute for Integrated Circuits IISErlangenGermany
  2. 2.Institute of PathologyUniversity Hospital Erlangen, FAU Erlangen-NurembergErlangenGermany
  3. 3.Department of SurgeryUniversity Hospital Erlangen, FAU Erlangen-NurembergErlangenGermany

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