Visual Deep Learning-Based Explanation for Neuritic Plaques Segmentation in Alzheimer’s Disease Using Weakly Annotated Whole Slide Histopathological Images

Gabriel Jimenez, Anuradha Kar, Mehdi Ounissi, Léa Ingrassia, Susana Boluda, Benoît Delatour, Lev Stimmer, Daniel Racoceanu

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

3 Citas (Scopus)

Resumen

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 (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.

Idioma originalInglés
Título de la publicación alojadaMedical Image Computing and Computer Assisted Intervention – MICCAI 2022 - 25th International Conference, Proceedings
EditoresLinwei Wang, Qi Dou, P. Thomas Fletcher, Stefanie Speidel, Shuo Li
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas336-344
Número de páginas9
ISBN (versión impresa)9783031164330
DOI
EstadoPublicada - 2022
Publicado de forma externa
Evento25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapur
Duración: 18 set. 202222 set. 2022

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen13432 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
País/TerritorioSingapur
CiudadSingapore
Período18/09/2222/09/22

Huella

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