Integration of Deep Learning and Graph Theory for Analyzing Histopathology Whole-slide Images

Abstract

Characterization of collagen deposition in immunostained images is relevant to various pathological conditions, particularly in human immunodeficiency virus (HIV) infection. Accurate segmentation of these collagens and extracting representative features of underlying diseases are important steps to achieve quantitative diagnosis. While a first order statistic derived from the segmented collagens can be useful in representing pathological evolutions at different timepoints, it fails to capture morphological changes and spatial arrangements. In this work, we demonstrate a complete pipeline for extracting key histopathology features representing underlying disease progression from histopathology whole-slide images (WSIs) via integration of deep learning and graph theory. A convolutional neural network is trained and utilized for histopathological WSI segmentation. Parallel processing is applied to convert 100K ~ 150K segmented collagen fibrils into a single collective attributed relational graph, and graph theory is applied to extract topological and relational information from the collagenous framework. Results are in good agreement with the expected pathogenicity induced by collagen deposition, highlighting potentials in clinical applications for analyzing various meshwork-structures in whole-slide histology images.