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The Devil Is in the Boundary: Boundary-Enhanced Polyp Segmentation

文献类型：期刊论文

作者Liu, Zhizhe1,2; Zheng, Shuai1,2; Sun, Xiaoyi1,2; Zhu, Zhenfeng1,2; Zhao, Yawei3; Yang, Xuebing4; Zhao, Yao1,2
刊名IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
出版日期2024-07-01
卷号34期号:7页码:5414-5423
关键词Polyp segmentation boundary enhancement feature refinement deformable Laplacian multi-level collaboration
ISSN号1051-8215
DOI10.1109/TCSVT.2023.3348598
通讯作者Zhu, Zhenfeng(zhfzhu@bjtu.edu.cn)
英文摘要Due to the various appearance of the polyps and the tiny contrast between the polyp area and its surrounding background, accurate polyp segmentation has become a challenging task. To tackle this issue, we introduce a boundary-enhanced framework for polyp segmentation, called the Focused on Boundary Segmentation (FoBS) framework, that leverages multi-level collaboration among sample, feature, and optimization. It places greater emphasis on the polyp boundary to improve the accuracy of segmentation. Firstly, a boundary-aware mixup method is designed to improve the model's awareness of the boundary. More importantly, we propose deformable laplacian-based feature refining to explicitly strengthen the representation ability of the boundary features. It employs a deformable Laplacian refinement function to capture discriminative information from a deformable perceptual field, thereby improving its ability to adapt to boundary variations. In addition, we introduce the self-adjusting refinement coefficient learning that enables adaptive control over the refinement strength at each location. Furthermore, we develop a location-sensitive compensation criterion that assigns more importance to the degraded feature after feature refinement during optimization. Extensive quantitative and qualitative experiments on four polyp benchmarks demonstrate the effectiveness of our method for automatic polyp segmentation. Our code is available at https://github.com/TFboys-lzz/FoBS.
WOS关键词IMAGES
资助项目Science and Technology Innovation 2030-New Generation Artificial Intelligence Major Project[2018AAA0102100] ; National Natural Science Foundation of China[61976018] ; National Natural Science Foundation of China[62302522] ; Beijing Natural Science Foundation[7222313] ; National High Level Hospital Clinical Research Funding[2022-PUMCH-C-041] ; Artificial Intelligence Public Service Platform for Screening and Auxiliary Diagnosis of Medical and Health Conditions[2020-0103-3-1]
WOS研究方向Engineering
语种英语
WOS记录号WOS:001263608800096
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构Science and Technology Innovation 2030-New Generation Artificial Intelligence Major Project ; National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National High Level Hospital Clinical Research Funding ; Artificial Intelligence Public Service Platform for Screening and Auxiliary Diagnosis of Medical and Health Conditions
源URL[http://ir.ia.ac.cn/handle/173211/59193]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Zhu, Zhenfeng
作者单位1.Beijing Jiaotong Univ, Beijing Key Lab Adv Informat Sci & Network Technol, Beijing 100044, Peoples R China
2.Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China
3.Chinese Peoples Liberat Army Gen Hosp, Med Big Data Res Ctr, Beijing 100853, Peoples R China
4.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Zhizhe,Zheng, Shuai,Sun, Xiaoyi,et al. The Devil Is in the Boundary: Boundary-Enhanced Polyp Segmentation[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2024,34(7):5414-5423.
APA Liu, Zhizhe.,Zheng, Shuai.,Sun, Xiaoyi.,Zhu, Zhenfeng.,Zhao, Yawei.,...&Zhao, Yao.(2024).The Devil Is in the Boundary: Boundary-Enhanced Polyp Segmentation.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,34(7),5414-5423.
MLA Liu, Zhizhe,et al."The Devil Is in the Boundary: Boundary-Enhanced Polyp Segmentation".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 34.7(2024):5414-5423.

入库方式： OAI收割

来源：自动化研究所

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