BDG-Net: boundary distribution guided network for accurate polyp segmentation
Zihuan Qiu, Zhichuan Wang, Miaomiao Zhang, Ziyong Xu, Jie Fan, Linfeng Xu
Abstract
Colorectal cancer (CRC) is one of the most common fatal cancer in the world. Polypectomy can effectively interrupt the progression of adenoma to adenocarcinoma. Colonoscopy is the primary method to find colonic polyps. However, due to the different sizes and the unclear boundary of polyps, it is challenging to segment polyps accurately. To this end, we design a Boundary Distribution Guided Network (BDG-Net) for accurate polyp segmentation. Specifically, Boundary Distribution Generate Module (BDGM) aggregates high-level features to generate Boundary Distribution Map (BDM), which is sent to the Boundary Distribution Guided Decoder (BDGD) as complementary spatial information to guide the polyp segmentation. Moreover, a multi-scale feature interaction strategy is adopted in BDGD to improve the polyp segmentation of different sizes. Extensive experiments demonstrate that BDG-Net outperforms state-of-the-art models remarkably and maintains low computational complexity.