BDG-Net: boundary distribution guided network for accurate polyp segmentation

Zihuan Qiu; Zhichuan Wang; Miaomiao Zhang; Ziyong Xu; Jie Fan; Linfeng Xu

doi:10.1117/12.2606785

4 April 2022 BDG-Net: boundary distribution guided network for accurate polyp segmentation

Zihuan Qiu, Zhichuan Wang, Miaomiao Zhang, Ziyong Xu, Jie Fan, Linfeng Xu

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 1203230 (2022) https://doi.org/10.1117/12.2606785
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

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.

Conference Presentation

Citation Download Citation

Zihuan Qiu, Zhichuan Wang, Miaomiao Zhang, Ziyong Xu, Jie Fan, and Linfeng Xu "BDG-Net: boundary distribution guided network for accurate polyp segmentation", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 1203230 (4 April 2022); https://doi.org/10.1117/12.2606785

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