BMFA-Net: Boundary Constraint Multi-Level Feature Aggregation Framework for Precise Polyp Segmentation

Abstract

Colorectal cancer is one of the three most common cancers worldwide. Polyps are complex and have unclear boundaries, often leading to inaccurate boundary segmentation and missed detections. To address these challenges, we propose a boundary constraint multi-level feature aggregation framework called BMFA-Net to precisely segment polyps. The framework comprises four key modules. First, the parallel partial decoder is introduced to aggregate high-level features within the network to generate a globally informative semantic map serving as the initial guidance region for reverse erasing the foreground. Second, we propose an efficient atrous convolution attention module to effectively aggregate local and global contextual information over multiple levels. Additionally, a multi-level feature aggregation mechanism is designed and placed among the efficient atrous convolution attention modules to enable the network to capture a large amount of semantic structure while preserving intricate details. Finally, a boundary constraint reverse attention module is proposed to perform the boundary constraint while removing the foreground to improve the quality of boundary segmentation. Extensive experiments demonstrated the superiority and versatility of our framework compared with state-of-the-art methods; specifically, it achieved a mean Dice score of 0.922 on the CVC-300 dataset.

Keywords

• polyp segmentation
• atrous convolution
• boundary constraint reverse attention
• multi-level feature aggregation