Atrous spatial pyramid pooling and multi-image data fusion for smooth muscle segmentation in upper gastrointestinal sphincters *

Gastrointestinal (GI) sphincters provide critical roles in regulating the transport of contents along the GI tract. Dysfunctions of GI sphincters are associated with a range of major digestive disorders. Despite their importance, the microstructures of GI sphincters are not well investigated. While micro-computed tomography (μ-CT) provides detailed 3D images, conventional segmentation methods rely on manual correction, which is both time-consuming and prone to human error. This study proposes a segmentation method using atrous spatial pyramid pooling (ASPP), which helps in capturing different effective fields of view from a given input feature map, thereof providing finer local and global information for a given pixel. Additionally, we explored the use of multi-species data fusion to make the model more generalized. The proposed segmentation network incorporating ASPP and multi-species data fusion improved the segmentation of sphincter muscle images. Specifically, it increased the dice score and Jaccard index by 3.7% and 5.8%, respectively, while reducing the variance compared to conventional methods.Clinical relevance— Techniques developed in this study will inform μ-CT segmentation of human upper GI sphincters for detailed structural analysis of muscular dysfunction.