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MFBGR: Multi-scale feature boundary graph reasoning network for polyp segmentation

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartA期123卷

作者： Liu, Fangjin Hua, Zhen Li, Jinjiang Fan, Linwei Shandong Technol & Business Univ Coinnovat Ctr Shandong Coll Univ Future Intelligen Sch Comp Sci & Technol Yantai 264005 Peoples R China Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan 250014 Shandong Peoples R China

At present, adding Transformer to CNN has promoted the rapid development of colorectal polyp image processing. However, from the perspective of multi-scale feature interaction and boundary coherence, there are mainly some limitations: (1) ignore the local and global correlation within the scale feature, which may cause the missed detection of tiny polyps, (2) lack of multi-scale features to explore the target region, which hinders the learning of multi-variant polyps, and (3) the semantic connection between the target area and the boundary is ignored, cause incoherent segmentation boundaries. In this regard, we design a multi-scale feature boundary graph inference network for polyp segmentation, namely MFBGR. First, the Transformer block captures local- global cues inside the multi-scale information learned by the CNN branches. Second, for the multi-scale global information generated by the Transformer block, we design a cross-scale feature fusion module (CSFM). CSFM performs scale-variation interaction and cascaded fusion to capture the correlation between features across scales and solve the scale-variation problem of segmented objects. Finally, the traditional boundary refinement or enhancement idea is generalized to the graph convolutional reasoning layer (BGRM). BGRM receives CNN's low-level feature information and CSFM's fusion features, or intermediate prediction results, and propagates cross-domain feature information between graph vertices, explores information between target regions and boundary regions, and achieves more accurate boundary segmentation. On the CVC-300, CVC-ClinicDB, CVC-ColonDB, Kvasir-SEG, ETIS datasets, MFBGR and mainstream polyp segmentation networks were compared and tested. MFBGR achieved good results, and Dice, IOU, BAcc, and Haudo were the best. The values reached 94.16%, 89.35% and 97.42%, 3.7442, and the segmentation accuracy of colorectal polyp images has been improved to a certain extent.

关键词： Polyp segmentation Transformer Convolutional Neural Network Cross-scale feature fusion module boundary graph reasoning module

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