Pyramid Pooling Channel Attention Network for esophageal tissue segmentation on OCT images

Abstract

The automatic segmentation of esophageal tissue layers in OCT images is essential for the study of esophageal diseases and computer-aided diagnosis. The tissue layer thickness of the esophagus is an important diagnostic sign for many esophageal diseases. Manually marking the boundary to calculate the average thickness of each layer is time-consuming and susceptible to subjective factors of the marker. In this paper, we propose a Pyramid Pooling Channel Attention Network (PPCANet) for the tissue segmentation. On the basis of the PSPNet, a channel attention module is introduced to selectively emphasize interdependent channel maps by integrating associated features among all channel maps, which makes the segmentation more precise. The potential clinical application of PPCANet for detecting eosinophilic esophagitis (EoE), an esophageal disease, is also presented in this paper.