SRSegNet: Super-resolution-assisted small targets polyp segmentation network with combined high and low resolution

Abstract

Accurate and reliable segmentation of polyp targets is crucial in the treatment of colorectal cancer. However, in clinical practice, polyp structures represent only a small portion of the image, which is essential for the accurate diagnosis and treatment of colorectal cancer. The small size of lesion areas leads to a reduction in feature representation, thereby affecting the performance of traditional segmentation methods. To address this issue, To address this issue, recent advancements in segmentation methods for small medical objects meticulously extract feature information from these objects using attention mechanisms. However, these methods mainly focus on general small medical objects and perform poorly in the segmentation of small polyps.Therefore, this study fully exploits the advantages of joint learning and multi-task learning. By guiding the high-resolution features to guide the low-resolution encoder through joint learning and fusing effective features from the super-resolution task to assist the segmentation task, our method enhances the model’s ability to extract texture details. We also design a high-resolution guidance module and an information complementation module, effectively integrating learning between different resolutions and tasks.Evaluation experiments on two settings on the Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, CVC-300, and ETIS datasets demonstrate that our method achieves significant advantages in the segmentation of small polyps, outperforming various state-of-the-art methods.

Keywords

• Joint learning
• Multi-task learning
• Super-resolution reconstruction
• Small target segmentation