Dual External Contextual Attention Network for Pseudomyxoma Peritonei Segmentation in CT Images

Abstract

Using computed tomography (CT) images to assist pseudomyxoma peritonei (PMP) diagnosis is noninvasive and fast compared with puncturing detection. However, it is time and energy-demanding to detect and annotate lesions on CT scans for radiologists. Thus, the automatic segmentation of PMP lesions is of great potential to reduce the burden on radiologists and improve PMP diagnostic efficiency. This paper proposed a Dual External Contextual Attention Network (DECANet) to segment PMP lesions automatically. Our network is derived from ResUNet, and we design a module named dual external contextual attention to extract high-level features to improve PMP lesion segmentation accuracy. The PMP segmentation dataset are collected from 38 patients and annotated by experienced radiologists. The proposed network achieves good performance with a dice coefficient of 88.68% and a mean Intersection over Union (mIoU) of 79.40%, outperforming other networks including UNet, AttentionUNet, and ResUNet.