Archives of pathology & laboratory medicine · 2024
A Deep Learning-Based Approach to Estimate Paneth Cell Granule Area in Celiac Disease.
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Alharbi Ebtihal, Rajaram Ajay, Côté Kevin, Farag Mina, Maleki Farhad, Gao Zu-Hua, Maedler-Kron Chelsea, Marcus Victoria, Fiset Pierre Olivier
Аннотация
CONTEXT.—: Changes in Paneth cell numbers can be associated with chronic inflammatory diseases of the gastrointestinal tract. So far, no consensus has been achieved on the number of Paneth cells and their relevance to celiac disease (CD).
OBJECTIVES.—: To compare crypt and Paneth cell granule areas between patients with CD and those without CD (non-CD) using an artificial intelligence-based solution.
DESIGN.—: Hematoxylin-eosin-stained sections of duodenal biopsies from 349 patients at the McGill University Health Centre were analyzed. Of these, 185 had a history of CD and 164 were controls. Slides were digitized, and NoCodeSeg, a code-free workflow using open-source software (QuPath, DeepMIB), was implemented to train deep learning models to segment crypts and Paneth cell granules. The total area of the entire analyzed tissue, epithelium, crypts, and Paneth cell granules was documented for all slides, and comparisons were performed.
RESULTS.—: A mean intersection-over-union score of 88.76% and 91.30% was achieved for crypt areas and Paneth cell granule segmentations, respectively. On normalization to total tissue area, the crypt to total tissue area in CD was increased and the Paneth cell granule area to total tissue area decreased when compared to non-CD controls.
CONCLUSIONS.—: Crypt hyperplasia was confirmed in CD compared to non-CD controls. The area of Paneth cell granules, an indirect measure of Paneth cell function, decreased with increasing severity of CD. More importantly, our study analyzed complete hematoxylin-eosin slide sections using an efficient and easy to use coding-free artificial intelligence workflow.
OBJECTIVES.—: To compare crypt and Paneth cell granule areas between patients with CD and those without CD (non-CD) using an artificial intelligence-based solution.
DESIGN.—: Hematoxylin-eosin-stained sections of duodenal biopsies from 349 patients at the McGill University Health Centre were analyzed. Of these, 185 had a history of CD and 164 were controls. Slides were digitized, and NoCodeSeg, a code-free workflow using open-source software (QuPath, DeepMIB), was implemented to train deep learning models to segment crypts and Paneth cell granules. The total area of the entire analyzed tissue, epithelium, crypts, and Paneth cell granules was documented for all slides, and comparisons were performed.
RESULTS.—: A mean intersection-over-union score of 88.76% and 91.30% was achieved for crypt areas and Paneth cell granule segmentations, respectively. On normalization to total tissue area, the crypt to total tissue area in CD was increased and the Paneth cell granule area to total tissue area decreased when compared to non-CD controls.
CONCLUSIONS.—: Crypt hyperplasia was confirmed in CD compared to non-CD controls. The area of Paneth cell granules, an indirect measure of Paneth cell function, decreased with increasing severity of CD. More importantly, our study analyzed complete hematoxylin-eosin slide sections using an efficient and easy to use coding-free artificial intelligence workflow.