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Boundary-Sensitive Loss Function with Location Constraint for Hard Region Segmentation
Jie Du1; Kai Guan1; Peng Liu2; Yuanman Li3; Tianfu Wang1
2022-11-15
Source PublicationIEEE Journal of Biomedical and Health Informatics
ISSN2168-2194
Volume27Issue:2Pages:992-1003
Abstract

In computer-aided diagnosis and treatment planning, accurate segmentation of medical images plays an essential role, especially for some hard regions including boundaries, small objects and background interference. However, existing segmentation loss functions including distribution-, region- and boundary-based losses cannot achieve satisfactory performances on these hard regions. In this paper, a boundary-sensitive loss function with location constraint is proposed for hard region segmentation in medical images, which provides three advantages: i) our Boundary-Sensitive loss (BS-loss) can automatically pay more attention to the hard-to-segment boundaries (e.g., thin structures and blurred boundaries), thus obtaining finer object boundaries; ii) BS-loss also can adjust its attention to small objects during training to segment them more accurately; and iii) our location constraint can alleviate the negative impact of the background interference, through the distribution matching of pixels between prediction and Ground Truth (GT) along each axis. By resorting to the proposed BS-loss and location constraint, the hard regions in both foreground and background are considered. Experimental results on three public datasets demonstrate the superiority of our method. Specifically, compared to the second-best method tested in this study, our method improves performance on hard regions in terms of Dice similarity coefficient (DSC) and 95% Hausdorff distance (95%HD) of up to 4.17% and 73% respectively. In addition, it also achieves the best overall segmentation performance. Hence, we can conclude that our method can accurately segment these hard regions and improve the overall segmentation performance in medical images.

KeywordHard Region Segmentation Intra-class Imbalance Boundary Sensitive Loss Location Constraint
DOI10.1109/JBHI.2022.3222390
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Mathematical & Computational Biology ; Medical Informatics
WOS SubjectComputer Science, Information systemsComputer Science, Interdisciplinary Applications;mathematical & Computational Biology;medical Informatics
WOS IDWOS:000967164200001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC445 HOES LANE, PISCATAWAY, NJ 08855-4141
Scopus ID2-s2.0-85142820548
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Citation statistics
Document TypeJournal article
CollectionFaculty of Science and Technology
DEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorYuanman Li
Affiliation1.NationalRegional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, Chin
2.Department of Computer and Information Science, University of Macau, Macau SAR 999078, China
3.Guangdong Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China
Recommended Citation
GB/T 7714
Jie Du,Kai Guan,Peng Liu,et al. Boundary-Sensitive Loss Function with Location Constraint for Hard Region Segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 2022, 27(2), 992-1003.
APA Jie Du., Kai Guan., Peng Liu., Yuanman Li., & Tianfu Wang (2022). Boundary-Sensitive Loss Function with Location Constraint for Hard Region Segmentation. IEEE Journal of Biomedical and Health Informatics, 27(2), 992-1003.
MLA Jie Du,et al."Boundary-Sensitive Loss Function with Location Constraint for Hard Region Segmentation".IEEE Journal of Biomedical and Health Informatics 27.2(2022):992-1003.
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