Multi-scale information sharing and selection network with boundary attention for polyp segmentation

Polyp segmentation in colonoscopy images is essential in clinical practice, offering valuable information for the diagnosis of colorectal cancer and subsequent surgical procedures. Despite the relatively good performance of existing methods, polyp segmentation still faces the following challenges: (1) Varying lighting conditions in colonoscopy and differences in polyp locations, sizes, and morphologies. (2) The indistinct boundary between polyps and surrounding tissue. To tackle these challenges, we propose a Multi-scale Information Sharing and Selection Network (MISNet) for the polyp segmentation task. We have designed a Selectively Shared Fusion Module (SSFM) to facilitate information sharing and the active selection between low-level and high-level features, thus enhancing the model’s ability to capture comprehensive information. Subsequently, we have developed a Parallel Attention Module (PAM) to improve the model’s attention on boundaries, and a Balancing Weight Module (BWM) to support the continuous refinement of boundary segmentation through the bottom-up process. Extensive experiments on five benchmark datasets show competitive results compared to existing representative methods. Specifically, our method has reached the mean Dice coefficient of 0.903 and 0.918 on the Kvasir and CVC-ClinicDB datasets, 0.762 and 0.764 on the challenging CVC-ColonDB and ETIS datasets. These innovative modules in our proposed MISNet effectively address key challenges, providing a robust solution for accurate polyp segmentation in clinical diagnosis and treatment. The proposed model is available at https://github.com/q1216355254/MISNet.git.