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9 March 2022 Numerical landmark detection algorithm for fringe projection profilometry during autonomous robotic suturing
Shuwen Wei, Michael Kam, Yaning Wang, Justin D. Opfermann, Hamed Saeidi, Michael H. Hsieh, Axel Krieger, Jin U. Kang
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Proceedings Volume PC11949, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XX; PC119490A (2022) https://doi.org/10.1117/12.2609134
Event: SPIE BiOS, 2022, San Francisco, California, United States
Abstract
Fringe projection profilometry (FPP) is being developed as a 3D vision system to assist robotic surgery and autonomous suturing. Conventionally, fluorescence markers are placed on a target tissue to indicate suturing landmarks, which not only increase the system complexity, but also impose safety concerns. To address these problems, we propose a numerical landmark detection algorithm based on deep learning. A landmark heatmap is regressed using an adopted U-Net from the four channel data generated by the FPP. A Markov random field leveraging the structure prior is developed to search the correct set of landmarks from the heatmap. The accuracy of the proposed method is verified through ex-vivo porcine intestine landmark detection experiments.
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Shuwen Wei, Michael Kam, Yaning Wang, Justin D. Opfermann, Hamed Saeidi, Michael H. Hsieh, Axel Krieger, and Jin U. Kang "Numerical landmark detection algorithm for fringe projection profilometry during autonomous robotic suturing", Proc. SPIE PC11949, Advanced Biomedical and Clinical Diagnostic and Surgical Guidance Systems XX, PC119490A (9 March 2022); https://doi.org/10.1117/12.2609134
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KEYWORDS
Detection and tracking algorithms
Robotics
Clouds
Luminescence
Reliability
Tissues
Light sources
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