TY - GEN
T1 - Feature extraction of video data for automatic visual tool tracking in robot assisted surgery
AU - Huang, J.
AU - Li, X.
AU - Kesavadas, T.
AU - Yang, L.
N1 - Funding Information:
This work was supported by the Zhejiang University/University of Illinois at Urbana-Champaign Institute, and Natural Science Foundation of Zhejiang Province (LQ19F030013) led by Principal Supervisor Liangjing Yang. We would also like to express our gratitude to Dr. David Crawford from OSF healthcare St. Francis Medical center in sharing the video segments for his robot-assisted minimally invasive procedures.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/7/26
Y1 - 2019/7/26
N2 - The strong interest in data-driven surgical procedures has motivated the need for automatically analyzing instrument trajectories during surgical procedures to improve the efficacy of modern robot-assisted surgeries. We proposed a single view camera-based approach for automatic video analysis of surgical instruments during robot-assisted minimally invasive procedures. This video data-driven approach uses feature extraction for visual tracking of the surgical instrument, coupled with the kinematics of the pivoted motion in minimally invasive surgery for tool trajectory analysis. The self-contained nature of the approach enables the acquisition of trajectory information without any prior placement of markers or post storage of robot joint trajectories information. Based on visual inspection of the 2D localization results in the image frames, the proposed method demonstrated reasonable recovery of the 3D positional information of the surgical tools. Driven by the increasingly sophisticated interventional procedures of modern surgeries, this proposed trajectory tracking method provides a means for leveraging the wealth of surgical video data for data analytics in Surgical Data Science. The long-term goal of the study is to contribute towards the advancement of Surgical Data Science through the establishment of a video-based data acquisition technique.
AB - The strong interest in data-driven surgical procedures has motivated the need for automatically analyzing instrument trajectories during surgical procedures to improve the efficacy of modern robot-assisted surgeries. We proposed a single view camera-based approach for automatic video analysis of surgical instruments during robot-assisted minimally invasive procedures. This video data-driven approach uses feature extraction for visual tracking of the surgical instrument, coupled with the kinematics of the pivoted motion in minimally invasive surgery for tool trajectory analysis. The self-contained nature of the approach enables the acquisition of trajectory information without any prior placement of markers or post storage of robot joint trajectories information. Based on visual inspection of the 2D localization results in the image frames, the proposed method demonstrated reasonable recovery of the 3D positional information of the surgical tools. Driven by the increasingly sophisticated interventional procedures of modern surgeries, this proposed trajectory tracking method provides a means for leveraging the wealth of surgical video data for data analytics in Surgical Data Science. The long-term goal of the study is to contribute towards the advancement of Surgical Data Science through the establishment of a video-based data acquisition technique.
KW - Feature Extraction
KW - Surgical Data Science
KW - Visual Tracking
UR - http://www.scopus.com/inward/record.url?scp=85073251765&partnerID=8YFLogxK
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U2 - 10.1145/3351180.3351185
DO - 10.1145/3351180.3351185
M3 - Conference contribution
AN - SCOPUS:85073251765
T3 - ACM International Conference Proceeding Series
SP - 121
EP - 127
BT - Proceedings of the 2019 4th International Conference on Robotics, Control and Automation, ICRCA 2019 - Workshop 2019 the 4th International Conference on Robotics and Machine Vision, ICRMV 2019
PB - Association for Computing Machinery
T2 - 2019 4th International Conference on Robotics, Control and Automation, ICRCA 2019 and its Workshop of 2019 4th International Conference on Robotics and Machine Vision, ICRMV 2019
Y2 - 26 July 2019 through 28 July 2019
ER -