A meta-graph approach for analyzing whole slide histopathological images of human brain tissue with Alzheimer's disease biomarkers

Abstract

International audienceRecently, high performance deep learning models have allowed automatic and precise analysis of high-content medical images. In digital histopathology, a typical challenge lies in analyzing whole slide images (WSI) due to their large dimensions which most often requires splitting them into small patches for feeding deep learning models. This leads to loss in global tissue level information and is particularly limiting to classification or clustering of patients based on tissue characteristics.In this study, a meta-graph approach is developed for a semantic spatial analysis of histopathological Whole Slide Images (WSI) of human brain tissue containing tau protein aggregates, one of the hallmark lesions of Alzheimer disease (AD) in brain gray matter. We propose a pipeline that extracts morphological features of tau aggregates like neuritic plaques or neurofibrillary tangles using a pre-trained U-Net model and uses these to build a graph based on Delaunay triangulation at the WSI level, in order to extract topological features from them. This pipeline is generating morphological and topological tabular data from WSI for classification and clustering patients. Further, combining locally extracted morphological features - at the neuritic plaques or neurofibrillary tangle level - with the Delaunay graph constructed at the WSI level, allows constructing a meta-graph that can be efficiently fed to graph neural network models, instead of the voluminous WSI. This pipeline is developed and tested on a dataset of 60 WSIs from various cohorts of patients having classic and rapidly advancing AD. The purpose of this pipeline is to identify novel insights into AD evolution, as well as provide a generic framework for creating knowledge rich graphs for WSI characterization and analysis