Multi-Scale Cross-Dimensional Attention Network for Gland Segmentation

Gland lesions affect a large global population. Accurately segmenting surface structures is crucial for assisting in the diagnosis of these diseases. In this direction, we investigate two key issues: 1) How to accurately segment gland morphology and irregular boundaries and 2) How to distinguish gland internal heterogeneity and its similarity to the background. The main results are that 1) parallel multi-scale attention (PMA) smooths the segmentation of blurred boundaries of varying sizes and improves detail accuracy. 2) Cross-dimensional attention (CDA) models the dependencies between gland channels and spatial dimensions to enhance the understanding of spatial information both inside and outside the gland, thereby more accurately distinguishing the gland from the background. Per the main results, we propose a multi-scale cross-dimensional attention network (MCANet) for gland segmentation. Extensive experiments on six real-world datasets demonstrate the superior performance of our method in gland segmentation.