Recently, high-performance deep learning models have enabled automatic and precise analysis of medical images with high content. In digital histopathology, a challenge lies in analyzing Whole Slide Images (WSI) due to their large size, often requiring splitting them into smaller patches for deep learning models. This leads to the loss of global tissue information and limits the classification or clustering of patients based on tissue characteristics.
In this study, we develop a meta-graph approach for semantic spatial analysis of WSI of human brain tissue containing tau protein aggregates, a hallmark of Alzheimer’s disease (AD) in gray matter. Our pipeline extracts morphological features of tau aggregates, such as forming neuritic plaques, and builds a graph based on Delaunay triangulation at the WSI level to extract topological features. This generates morphological and topological data from WSI for patient classification and clustering. We tested this pipeline on a dataset of 15 WSIs from different AD patients. We aim to identify new insights into AD evolution and provide a generic framework for WSI characterization and analysis.
Tau proteins in the gray matter are widely known to be a part of Alzheimer’s disease symptoms. They can aggregate in three different structures within the brain: neurites, tangles, and neuritic plaques. The morphology and the spatial disposition of these three aggregates are hypothesised to be correlated to the advancement of the disease. In order to establish a behavioural disease model related to the Tau proteins aggregates, it is necessary to develop algorithms to detect and segment them automatically. We present a 5-folded pipeline aiming to perform with clinically operational results. This pipeline is composed of a non-linear colour normalisation, a CNN-based image classifier, an Unet-based image segmentation stage, and a morphological analysis of the segmented objects. The tangle detection and segmentation algorithms improve state-of-the-art performances (75.8% and 91.1% F1- score, respectively), and create a reference for neuritic plaques detection and segmentation (81.3% and 78.2% F1-score, respectively). These results constitute an initial baseline in an area where no prior results exist, as far as we know. The pipeline is complete and based on a promising state-of-the-art architecture. Therefore, we consider this study a handy baseline of an impactful extension to support new advances in Alzheimer’s disease. Moreover, building a fully operational pipeline will be crucial to create a 3D histology map for a deeper understanding of clinico-pathological associations in Alzheimer’s disease and the histology-based evidence of disease stratification among different sub-types.
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