Tracking Everything in Robotic-Assisted Surgery

• URL: http://arxiv.org/abs/2409.19821v1
• Date: Sun, 29 Sep 2024 23:06:57 GMT
• Title: Tracking Everything in Robotic-Assisted Surgery
• Authors: Bohan Zhan, Wang Zhao, Yi Fang, Bo Du, Francisco Vasconcelos, Danail Stoyanov, Daniel S. Elson, Baoru Huang,
• Abstract summary: We present an annotated surgical tracking dataset for benchmarking tracking methods for surgical scenarios. We evaluate state-of-the-art (SOTA) TAP-based algorithms on this dataset and reveal their limitations in challenging surgical scenarios. We propose a new tracking method, namely SurgMotion, to solve the challenges and further improve the tracking performance.
• Score: 39.62251870446397
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate tracking of tissues and instruments in videos is crucial for Robotic-Assisted Minimally Invasive Surgery (RAMIS), as it enables the robot to comprehend the surgical scene with precise locations and interactions of tissues and tools. Traditional keypoint-based sparse tracking is limited by featured points, while flow-based dense two-view matching suffers from long-term drifts. Recently, the Tracking Any Point (TAP) algorithm was proposed to overcome these limitations and achieve dense accurate long-term tracking. However, its efficacy in surgical scenarios remains untested, largely due to the lack of a comprehensive surgical tracking dataset for evaluation. To address this gap, we introduce a new annotated surgical tracking dataset for benchmarking tracking methods for surgical scenarios, comprising real-world surgical videos with complex tissue and instrument motions. We extensively evaluate state-of-the-art (SOTA) TAP-based algorithms on this dataset and reveal their limitations in challenging surgical scenarios, including fast instrument motion, severe occlusions, and motion blur, etc. Furthermore, we propose a new tracking method, namely SurgMotion, to solve the challenges and further improve the tracking performance. Our proposed method outperforms most TAP-based algorithms in surgical instruments tracking, and especially demonstrates significant improvements over baselines in challenging medical videos.

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