Segmentation of Muscularis Propria in Colon Histopathology Images Using Vision Transformers for Hirschsprung's Disease
Journal:
arXiv
Published Date:
Dec 29, 2024
Abstract
Hirschsprung's disease (HD) 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 score of 89.9% and Plexus
Inclusion Rate (PIR) of 100%, surpassing the CNN (DICE score of 89.2%; PIR of
96.0%) and k-means clustering method (DICE score of 80.7%; PIR 77.4%). Results
assert that ViTs are a promising tool for advancing HD-related image analysis.
