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

: Bergler, M.; Benz, M.; Rauber, D.; Hartmann, D.; Kötter, M.; Eckstein, M.; Schneider-Stock, R.; Hartmann, A.; Merkel, S.; Bruns, V.; Wittenberg, T.; Geppert, C.


Reyes-Aldasoro, C.C.:
Digital Pathology. 15th European Congress, ECDP 2019. Proceedings : Warwick, UK, April 10-13, 2019
Cham: Springer Nature, 2019 (Lecture Notes in Computer Science 11435)
ISBN: 978-3-030-23936-7 (Print)
ISBN: 978-3-030-23937-4 (Online)
ISBN: 3-030-23936-5
European Congress on Digital Pathology (ECDP) <15, 2019, Warwick>
Conference Paper
Fraunhofer IIS ()

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.