Mitosis Detection for Invasive Breast Cancer Grading in Histopathological Images

TLDR

Histopathological grading of invasive breast cancer relies on mitosis counts, a laborious and variable process that requires pathologists to manually examine thousands of images to inform prognosis and treatment plans. The study proposes a fast, accurate method for automatic mitosis detection in histopathological images. The method segments cells using an area morphological scale space optimized by maximizing relative entropy between cells and background, then classifies them as mitotic or non‑mitotic with a random‑forest classifier. The approach yields precise cell segmentation and achieves at least a 12% improvement in F1 score on more than 450 histopathological images at 40× magnification.

Abstract

Histopathological grading of cancer not only offers an insight to the patients' prognosis but also helps in making individual treatment plans. Mitosis counts in histopathological slides play a crucial role for invasive breast cancer grading using the Nottingham grading system. Pathologists perform this grading by manual examinations of a few thousand images for each patient. Hence, finding the mitotic figures from these images is a tedious job and also prone to observer variability due to variations in the appearances of the mitotic cells. We propose a fast and accurate approach for automatic mitosis detection from histopathological images. We employ area morphological scale space for cell segmentation. The scale space is constructed in a novel manner by restricting the scales with the maximization of relative-entropy between the cells and the background. This results in precise cell segmentation. The segmented cells are classified in mitotic and non-mitotic category using the random forest classifier. Experiments show at least 12% improvement in F1 score on more than 450 histopathological images at 40× magnification.