ASGNet: Adaptive Spectrum Guidance Network for Automatic Polyp Segmentation

TL;DR

ASGNet is a novel deep learning model that enhances polyp segmentation in colonoscopy images by integrating spectral features and global information, outperforming existing methods.

Contribution

The paper introduces ASGNet, which combines spectral guidance, multi-source semantic extraction, and dense cross-layer interaction to improve polyp segmentation accuracy.

Findings

ASGNet outperforms 21 state-of-the-art methods across five benchmarks.

Spectral features improve the discrimination of polyp structures.

Global and local information integration enhances segmentation quality.

Abstract

Early identification and removal of polyps can reduce the risk of developing colorectal cancer. However, the diverse morphologies, complex backgrounds and often concealed nature of polyps make polyp segmentation in colonoscopy images highly challenging. Despite the promising performance of existing deep learning-based polyp segmentation methods, their perceptual capabilities remain biased toward local regions, mainly because of the strong spatial correlations between neighboring pixels in the spatial domain. This limitation makes it difficult to capture the complete polyp structures, ultimately leading to sub-optimal segmentation results. In this paper, we propose a novel adaptive spectrum guidance network, called ASGNet, which addresses the limitations of spatial perception by integrating spectral features with global attributes. Specifically, we first design a spectrum-guided non-local perception module that jointly aggregates local and global information, therefore enhancing the discriminability of polyp structures, and refining their boundaries. Moreover, we introduce a multi-source semantic extractor that integrates rich high-level semantic information to assist in the preliminary localization of polyps. Furthermore, we construct a dense cross-layer interaction decoder that effectively integrates diverse information from different layers and strengthens it to generate high-quality representations for accurate polyp segmentation. Extensive quantitative and qualitative results demonstrate the superiority of our ASGNet approach over 21 state-of-the-art methods across five widely-used polyp segmentation benchmarks. The code will be publicly available at: https://github.com/CSYSI/ASGNet.