@inproceedings{60a4b1798857477099409ad8518925c8,
title = "MVD-Net: Semantic Segmentation of Cataract Surgery Using Multi-View Learning",
abstract = "Semantic segmentation of surgery scenarios is a fundamental task for computer-aided surgery systems. Precise segmentation of surgical instruments and anatomies contributes to capturing accurate spatial information for tracking. However, uneven reflection and class imbalance lead the segmentation in cataract surgery to a challenging task. To desirably conduct segmentation, a network with multi-view decoders (MVD-Net) is proposed to present a generalizable segmentation for cataract surgery. Two discrepant decoders are implemented to achieve multi-view learning with the backbone of U-Net. The experiment is carried out on the Cataract Dataset for Image Segmentation (CaDIS). The ablation study verifies the effectiveness of the proposed modules in MVD-Net, and superior performance is provided by MVD-Net in the comparison with the state-of-the-art methods. The source code will be publicly released.",
author = "Mingyang Ou and Heng Li and Haofeng Liu and Xiaoxuan Wang and Chenlang Yi and Luoying Hao and Yan Hu and Jiang Liu",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 ; Conference date: 11-07-2022 Through 15-07-2022",
year = "2022",
doi = "10.1109/EMBC48229.2022.9871673",
language = "English",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5035--5038",
booktitle = "44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022",
address = "United States",
}