  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.
