A deep learning framework for context-aware mitotic activity estimation in whole slide images

Abstract

A proper cancer diagnosis is imperative for determining the medical treatment for a patient. It necessitates a good staging and classification of the tumor alongside with additional factors to predict response to treatment. Mitotic count-based tumor proliferation grade provides the most reproducible and independent prognostic value. In practice, pathologists examine H&E-stained, giga-pixel-sized digital whole-slide images of a tissue specimen for counting the mitotic index. Considering the enormity of the images, focus for analysis is centered on specific, so-called, high-power-fields (HPFs) on the periphery of the invasive parts of the tumor. Selection of the HPFs is very subjective. Additionally, tumor heterogeneity impacts both the region selection and the quality of the area analyzed. Several efforts have been made to automate the tumor proliferation score estimation by counting the mitotic figures in certain regions-of-interest. But the region selection algorithms are inconspicuous and do not ensure to encompass the crucial regions interesting for pathological analysis, thereby, making the grading sub-optimal. In this work, we aim at addressing this problem by proposing to visualize a distance weighted mitotic distribution in the entire invasive tumor region. Our approach provides a holistic view of the mitotic activity and localizes active proliferating regions in the tumor with tissue architecture context, enabling the pathologists to more objectively select the HPFs. We propose a deep learning-based framework to generate the mitotic activity heat-maps. Additionally, in the framework, we develop a number of significant tools for digital pathology; a semi-supervised tumor region delineation tool, a fast nuclei segmentation and detection tool, and a mitotic figure localization tool.