SegT: A Novel Separated Edge-guidance Transformer Network for Polyp Segmentation

TL;DR

SegT introduces a transformer-based polyp segmentation model with separated edge-guidance modules, effectively capturing local and global features to improve accuracy in challenging datasets.

Contribution

The paper proposes a novel transformer network with separated edge-guidance modules for improved polyp segmentation, addressing limitations of existing CNN-based methods.

Findings

Achieved state-of-the-art performance on five public datasets.

Effectively captures both local and global features for precise segmentation.

Utilizes a cascade fusion module for multi-level feature refinement.

Abstract

Accurate segmentation of colonoscopic polyps is considered a fundamental step in medical image analysis and surgical interventions. Many recent studies have made improvements based on the encoder-decoder framework, which can effectively segment diverse polyps. Such improvements mainly aim to enhance local features by using global features and applying attention methods. However, relying only on the global information of the final encoder block can result in losing local regional features in the intermediate layer. In addition, determining the edges between benign regions and polyps could be a challenging task. To address the aforementioned issues, we propose a novel separated edge-guidance transformer (SegT) network that aims to build an effective polyp segmentation model. A transformer encoder that learns a more robust representation than existing CNN-based approaches was specifically applied. To determine the precise segmentation of polyps, we utilize a separated edge-guidance module consisting of separator and edge-guidance blocks. The separator block is a two-stream operator to highlight edges between the background and foreground, whereas the edge-guidance block lies behind both streams to strengthen the understanding of the edge. Lastly, an innovative cascade fusion module was used and fused the refined multi-level features. To evaluate the effectiveness of SegT, we conducted experiments with five challenging public datasets, and the proposed model achieved state-of-the-art performance.