BDG-Net: Boundary Distribution Guided Network for Accurate Polyp Segmentation

TL;DR

BDG-Net is a novel deep learning model that leverages boundary distribution guidance and multi-scale feature interaction to improve the accuracy of polyp segmentation in colonoscopy images, addressing boundary ambiguity and size variability.

Contribution

The paper introduces BDG-Net, which uses boundary distribution supervision and multi-scale features to enhance polyp segmentation accuracy over existing methods.

Findings

Outperforms state-of-the-art models on five public datasets.

Effectively handles polyps of different sizes.

Maintains low computational complexity.

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, 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