Visual deep learning-based explanation for neuritic plaques segmentation in Alzheimer’s Disease using weakly annotated whole slide histopathological images. (arXiv:2302.08511v1 [eess.IV])

Quantifying 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 (url{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.