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Quantifying the distribution and morphology of tau protein structures inbrain tissues is key to diagnosing Alzheimer's Disease (AD) and its subtypes.Recently, deep learning (DL) models such as UNet have been successfully usedfor automatic segmentation of histopathological whole slide images (WSI) ofbiological tissues. In this study, we propose a DL-based methodology forsemantic segmentation of tau lesions (i.e., neuritic plaques) in WSI ofpostmortem patients with AD. The state of the art in semantic segmentation ofneuritic plaques in human WSI is very limited. Our study proposes a baselineable to generate a significant advantage for morphological analysis of thesetauopathies for further stratification of AD patients. Essential discussionsconcerning biomarkers (ALZ50 versus AT8 tau antibodies), the imaging modality(different slide scanner resolutions), and the challenge of weak annotationsare addressed within this seminal study. The analysis of the impact of contextin plaque segmentation is important to understand the role of themicro-environment for reliable tau protein segmentation. In addition, byintegrating visual interpretability, we are able to explain how the networkfocuses on a region of interest (ROI), giving additional insights topathologists. Finally, the release of a new expert-annotated database and thecode (\url{https://github.com/aramis-lab/miccai2022-stratifiad.git}) will behelpful for the scientific community to accelerate the development of newpipelines for human WSI processing in AD.

technologies logicielles architectures des systèmes

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