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

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

Visual deep learning-based explanation for neuritic plaques segmentation in Alzheimer's Disease using weakly annotated whole slide histopathological images

International audienceQuantifying the distribution and morphology of tau protein structures in brain tissues is key to diagnosing Alzheimer's Disease (AD) and its subtypes. Recently, deep learning (DL) models such as UNet have been successfully used for automatic segmentation of histopathological whole slide images (WSI) of biological tissues. In this study, we propose a DL-based methodology for semantic segmentation of tau lesions (i.e., neuritic plaques) in WSI of postmortem patients with AD. The state of the art in semantic segmentation of neuritic plaques in human WSI is very limited. Our study proposes a baseline able to generate a significant advantage for morphological analysis of these tauopathies for further stratification of AD patients. Essential discussions concerning biomarkers (ALZ50 versus AT8 tau antibodies), the imaging modality (different slide scanner resolutions), and the challenge of weak annotations are addressed within this seminal study. The analysis of the impact of context in plaque segmentation is important to understand the role of the micro-environment for reliable tau protein segmentation. In addition, by integrating visual interpretability, we are able to explain how the network focuses on a region of interest (ROI), giving additional insights to pathologists. Finally, the release of a new expert-annotated database and the code (https://github.com/aramis-lab/miccai2022-stratifiad.git) will be helpful for the scientific community to accelerate the development of new pipelines for human WSI processing in AD