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.

本研究针对先天性无神经节病在结肠固有肌层缺乏神经细胞的问题，提出使用视觉变换器（ViT）来进行图像分割，以提高病理图像的定量评估效率。通过与卷积神经网络（CNN）及其他浅层学习方法比较，ViT模型在分割精度和灵敏度上均有所提升，显示了ViT在HD相关图像分析中的应用潜力。

使用视觉变换器对结肠组织病理图像中的固有肌层进行分割以研究先天性无神经节病