Co-generation and Segmentation for Generalized Surgical Instrument Segmentation on Unlabelled Data
Kalia M, Aleef TA, Navab N, Black P, Salcudean SE (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12904 LNCS
Pages Range: 403-412
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783030872014
DOI: 10.1007/978-3-030-87202-1_39
Abstract
Surgical instrument segmentation for robot-assisted surgery is needed for accurate instrument tracking and augmented reality overlays. Therefore, the topic has been the subject of a number of recent papers in the CAI community. Deep learning-based methods have shown state-of-the-art performance for surgical instrument segmentation, but their results depend on labelled data. However, labelled surgical data is of limited availability and is a bottleneck in surgical translation of these methods. In this paper, we demonstrate the limited generalizability of these methods on different datasets, including robot-assisted surgeries on human subjects. We then propose a novel joint generation and segmentation strategy to learn a segmentation model with better generalization capability to domains that have no labelled data. The method leverages the availability of labelled data in a different domain. The generator does the domain translation from the labelled domain to the unlabelled domain and simultaneously, the segmentation model learns using the generated data while regularizing the generative model. We compared our method with state-of-the-art methods and showed its generalizability on publicly available datasets and on our own recorded video frames from robot-assisted prostatectomies. Our method shows consistently high mean Dice scores on both labelled and unlabelled domains when data is available only for one of the domains.
Involved external institutions
University of British Columbia
Canada (CA)
Technische Universität München (TUM)
Germany (DE)
Vancouver Prostate Centre
Canada (CA)
Canada (CA)
Technische Universität München (TUM)
Germany (DE)
Vancouver Prostate Centre
Canada (CA)
How to cite
APA:
Kalia, M., Aleef, T.A., Navab, N., Black, P., & Salcudean, S.E. (2021). Co-generation and Segmentation for Generalized Surgical Instrument Segmentation on Unlabelled Data. In Marleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 403-412). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Kalia, Megha, et al. "Co-generation and Segmentation for Generalized Surgical Instrument Segmentation on Unlabelled Data." Proceedings of the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Marleen de Bruijne, Marleen de Bruijne, Philippe C. Cattin, Stéphane Cotin, Nicolas Padoy, Stefanie Speidel, Yefeng Zheng, Caroline Essert, Springer Science and Business Media Deutschland GmbH, 2021. 403-412.
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