Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Classification, Reconstruction, and Tracking

• URL: http://arxiv.org/abs/2210.16674v1
• Date: Sat, 29 Oct 2022 19:33:21 GMT
• Title: Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Classification, Reconstruction, and Tracking
• Authors: Shan Lin, Albert J. Miao, Jingpei Lu, Shunkai Yu, Zih-Yun Chiu, Florian Richter, Michael C. Yip
• Abstract summary: We present a novel surgical perception framework, Semantic-SuPer. It integrates geometric and semantic information to facilitate data association, 3D reconstruction, and tracking of endoscopic scenes.
• Score: 21.133420628173067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and robust tracking and reconstruction of the surgical scene is a critical enabling technology toward autonomous robotic surgery. Existing algorithms for 3D perception in surgery mainly rely on geometric information, while we propose to also leverage semantic information inferred from the endoscopic video using image segmentation algorithms. In this paper, we present a novel, comprehensive surgical perception framework, Semantic-SuPer, that integrates geometric and semantic information to facilitate data association, 3D reconstruction, and tracking of endoscopic scenes, benefiting downstream tasks like surgical navigation. The proposed framework is demonstrated on challenging endoscopic data with deforming tissue, showing its advantages over our baseline and several other state-of the-art approaches. Our code and dataset will be available at https://github.com/ucsdarclab/Python-SuPer.

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