Segmentation of Muscularis Propria in Colon Histopathology Images Using Vision Transformers for Hirschsprung’s Disease

Abstract

Hirschsprung’s disease is a congenital birth defect

diagnosed by identifying the lack of ganglion cells within the

colon’s muscularis propria, specifically within the myenteric

plexus regions. There may be advantages for quantitative

assessments of histopathology images of the colon, such as

counting the ganglion and assessing their spatial distribution;

however, this would be time-intensive for pathologists, costly,

and subject to inter- and intra-rater variability. Previous

research has demonstrated the potential for deep learning

approaches to automate histopathology image analysis,

including segmentation of the muscularis propria using

Convolutional Neural Networks (CNNs). Recently, Vision

Transformers (ViTs) have emerged as a powerful deep learning

approach due to their self-attention. This study explores the

application of ViTs for muscularis propria segmentation in

calretinin-stained histopathology images and compares their

performance to CNNs and shallow learning methods. The ViT

model achieved a Dice-Sørensen coefficient of 89.9% and Plexus

Inclusion Rate (PIR) of 100%, surpassing the CNN (Dice-

Sørensen coefficient of 89.2%, PIR of 96.0%) and k-means

clustering method (Dice-Sørensen coefficient of 80.7%; PIR

77.4%). Results assert that ViTs are a promising tool for

advancing Hirschsprung’s disease-related image analysis.