  Colorectal cancer (CRC) is one of the most common fatal cancer in the world.
Polypectomy can effectively interrupt the progression of adenoma to
adenocarcinoma, thus reducing the risk of CRC development. Colonoscopy is the
primary method to find colonic polyps. However, due to the different sizes of
polyps and the unclear boundary between polyps and their surrounding mucosa, it
is challenging to segment polyps accurately. To address this problem, we design
a Boundary Distribution Guided Network (BDG-Net) for accurate polyp
segmentation. Specifically, under the supervision of the ideal Boundary
Distribution Map (BDM), we use Boundary Distribution Generate Module (BDGM) to
aggregate high-level features and generate BDM. Then, BDM 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 segmentation accuracy of
polyps with different sizes. Extensive quantitative and qualitative evaluations
demonstrate the effectiveness of our model, which outperforms state-of-the-art
models remarkably on five public polyp datasets while maintaining low
computational complexity. Code: https://github.com/zihuanqiu/BDG-Net
