Automated Histopathologic Assessment of Hirschsprung Disease Using a Multi-Stage Vision Transformer Framework

• URL: http://arxiv.org/abs/2511.20734v1
• Date: Tue, 25 Nov 2025 15:53:47 GMT
• Title: Automated Histopathologic Assessment of Hirschsprung Disease Using a Multi-Stage Vision Transformer Framework
• Abstract summary: Hirschsprung Disease is characterized by the absence of ganglion cells in the myenteric plexus.<n>We introduce a three-stage segmentation framework based on a Vision Transformer (ViT-B/16) that mimics the pathologist's diagnostic approach.
• Score: 2.360330081206126
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hirschsprung Disease is characterized by the absence of ganglion cells in the myenteric plexus. Therefore, their correct identification is crucial for diagnosing Hirschsprung disease. We introduce a three-stage segmentation framework based on a Vision Transformer (ViT-B/16) that mimics the pathologist's diagnostic approach. The framework sequentially segments the muscularis propria, delineates the myenteric plexus, and identifies ganglion cells within anatomically valid regions. 30 whole-slide images of colon tissue were used, each containing expert manual annotations of muscularis, plexus, and ganglion cells at varying levels of certainty. A 5-fold cross-validation scheme was applied to each stage, along with resolution-specific tiling strategies and tailored postprocessing to ensure anatomical consistency. The proposed method achieved a Dice coefficient of 89.9% and a Plexus Inclusion Rate of 100% for muscularis segmentation. Plexus segmentation reached a recall of 94.8%, a precision of 84.2% and a Ganglia Inclusion Rate of 99.7%. For high-certainty ganglion cells, the model achieved 62.1% precision and 89.1% recall, while joint certainty scores yielded 67.0% precision. These results indicate that ViT-based models are effective at leveraging global tissue context and capturing cellular morphology at small scales, even within complex histological tissue structures. This multi-stage methodology has great potential to support digital pathology workflows by reducing inter-observer variability and assisting in the evaluation of Hirschsprung disease. The clinical impact will be evaluated in future work with larger multi-center datasets and additional expert annotations.

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