DBMF: Dual Branch Multiscale Feature Fusion Network for polyp segmentation. (December 2022)

• Record Type: Journal Article
• Authors: Liu, Fangjin; Hua, Zhen; Li, Jinjiang; Fan, Linwei
• Is Part Of: Computers in biology and medicine. Volume 151:Part A(2022)
• Publication Date: 2022-12
• Subjects: Polyp segmentation -- Transformer -- Convolutional Neural Network -- Multi-scale feature aggregation -- Attention mechanism; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106304
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Accurate and reliable segmentation of colorectal polyps is important for the diagnosis and treatment of colorectal cancer. Most of the existing polyp segmentation methods innovatively combine CNN with Transformer. Due to the single combination approach, there are limitations in establishing connections between local feature information and utilizing global contextual information captured by Transformer. Still not a better solution to the problems in polyp segmentation. In this paper, we propose a Dual Branch Multiscale Feature Fusion Network for Polyp Segmentation, abbreviated as DBMF, for polyp segmentation to achieve accurate segmentation of polyps. DBMF uses CNN and Transformer in parallel to extract multi-scale local information and global contextual information respectively, with different regions and levels of information to make the network more accurate in identifying polyps and their surrounding tissues. Feature Super Decoder (FSD) fuses multi-level local features and global contextual information in dual branches to fully exploit the potential of combining CNN and Transformer to improve the network's ability to parse complex scenes and the detection rate of tiny polyps. The FSD generates an initial segmentation map to guide the second parallel decoder (SPD) to refine the segmentation boundary layer by layer. SPD consists of a multi-scale feature aggregation module (MFA) and parallel polarized self-attention (PSA) and reverse attention fusion modules (RAF). MFA aggregates multi-level local feature information extracted by CNN Brach to find consensus regions between multiple scales and improve the network's ability to identify polyp regions. PSA uses dual attention to enhance the fine-grained nature of segmented regions and reduce the redundancy introduced by MFA and interference information. RAF mines boundary cues and establishes relationships between regions and boundary cues. The three RAFs guide the network to explore lost targets and boundaries in a bottom-up manner. We used the CVC-ClinicDB, Kvasir, CVC-300, CVC-ColonDB, and ETIS datasets to conduct comparison experiments and ablation experiments between DBMF and mainstream polyp segmentation networks. The results showed that DBMF outperformed the current mainstream networks on five benchmark datasets. Highlights: Dual Branch Multiscale Feature Fusion Network, Multi-scale feature aggregation. Feature super decoder to fuse global contextual information. Polarized self-attentive module to mine more local–global relationships. Second parallel decoder to refine the initial segmentation prediction map.