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

TL;DR

This paper presents a multi-stage Vision Transformer framework for automated histopathologic assessment of Hirschsprung disease, accurately identifying ganglion cells and relevant tissue structures to support digital pathology workflows.

Contribution

It introduces a novel three-stage ViT-based analysis framework that mimics pathologist diagnosis, improving accuracy and consistency in detecting key histological features.

Findings

Achieved 89.9% Dice coefficient for muscularis segmentation

Reached 94.8% recall in plexus segmentation

Attained 62.1% precision and 89.1% recall for high-certainty ganglion cells

Abstract

Hirschsprung Disease is characterized by the absence of ganglion cells in the myenteric plexus. Therefore, the correct identification of ganglion cells is crucial for diagnosing Hirschsprung disease. We introduce a three-stage analysis framework that mimics the pathologist's diagnostic approach. The framework, based on a Vision Transformer model (ViT-B/16), sequentially segments the muscularis propria, segments the myenteric plexus, and detects ganglion cells within anatomically valid regions. 30 whole-slide images of colon tissue were used, each containing manual annotations of muscularis, plexus, and ganglion cells. 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 ganglion cells annotated with high certainty, the model achieved 62.1\% precision and 89.1% recall. When considering all annotated ganglion cells, regardless of certainty level, the overall precision was 67.0%. 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.