Automated Volume Corrected Mitotic Index Calculation Through
  Annotation-Free Deep Learning using Immunohistochemistry as Reference
  Standard
Jonas Ammeling Moritz Hecker Jonathan Ganz Taryn A. Donovan Christof A. Bertram Katharina Breininger Marc Aubreville
The volume-corrected mitotic index (M/V-Index) was shown to provide
prognostic value in invasive breast carcinomas. However, despite its prognostic
significance, it is not established as the standard method for assessing
aggressive biological behaviour, due to the high additional workload associated
with determining the epithelial proportion. In this work, we show that using a
deep learning pipeline solely trained with an annotation-free,
immunohistochemistry-based approach, provides accurate estimations of
epithelial segmentation in canine breast carcinomas. We compare our automatic
framework with the manually annotated M/V-Index in a study with three
board-certified pathologists. Our results indicate that the deep learning-based
pipeline shows expert-level performance, while providing time efficiency and
reproducibility.