Automated Volume Corrected Mitotic Index Calculation Through Annotation-Free Deep Learning using Immunohistochemistry as Reference Standard
This work addresses a bottleneck in pathology by automating a prognostic tool for breast cancer assessment, though it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackled the high workload of manually determining epithelial proportion for volume-corrected mitotic index (M/V-Index) in breast carcinomas by developing an annotation-free deep learning pipeline for epithelial segmentation, achieving expert-level performance with improved time efficiency and reproducibility.
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.