Adaptive Semi-supervised Segmentation of Brain Vessels with Ambiguous Labels
Lin F, Xia Y, Ravikumar N, Liu Q, MacRaild M, Frangi AF (2024)
Publication Type: Conference contribution
Publication year: 2024
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 14379 LNCS
Pages Range: 106-116
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Vancouver, BC, CAN
ISBN: 9783031581700
DOI: 10.1007/978-3-031-58171-7_11
Abstract
Accurate segmentation of brain vessels is crucial for cerebrovascular disease diagnosis and treatment. However, existing methods face challenges in capturing small vessels and handling datasets that are partially or ambiguously annotated. In this paper, we propose an adaptive semi-supervised approach to address these challenges. Our approach incorporates innovative techniques including progressive semi-supervised learning, adaptative training strategy, and boundary enhancement. Experimental results on 3DRA datasets demonstrate the superiority of our method in terms of mesh-based segmentation metrics. By leveraging the partially and ambiguously labeled data, which only annotates the main vessels, our method achieves impressive segmentation performance on mislabeled fine vessels, showcasing its potential for clinical applications.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Lin, F., Xia, Y., Ravikumar, N., Liu, Q., MacRaild, M., & Frangi, A.F. (2024). Adaptive Semi-supervised Segmentation of Brain Vessels with Ambiguous Labels. In Yuan Xue, Chen Chen, Chao Chen, Lianrui Zuo, Yihao Liu (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 106-116). Vancouver, BC, CAN: Springer Science and Business Media Deutschland GmbH.
MLA:
Lin, Fengming, et al. "Adaptive Semi-supervised Segmentation of Brain Vessels with Ambiguous Labels." Proceedings of the 3rd International Workshop on Data Augmentation, Labeling, and Imperfections, DALI 2023 in conjunction with the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023, Vancouver, BC, CAN Ed. Yuan Xue, Chen Chen, Chao Chen, Lianrui Zuo, Yihao Liu, Springer Science and Business Media Deutschland GmbH, 2024. 106-116.
BibTeX: Download