TY - GEN
T1 - Autonomous Positioning of Eye Surgical Robot Using the Tool Shadow and Kalman Filtering
AU - Tayama, Takashi
AU - Kurose, Yusuke
AU - Marinho, Murilo M.
AU - Koyama, Yuki
AU - Harada, Kanako
AU - Omata, Seiji
AU - Arai, Fumihito
AU - Sugimoto, Koichiro
AU - Araki, Fumiyuki
AU - Totsuka, Kiyohito
AU - Takao, Muneyuki
AU - Aihara, Makoto
AU - Mitsuishi, Mamoru
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - Vitreoretinal surgery is one of the most difficult surgical operations, even for experienced surgeons. Thus, a master-slave eye surgical robot has been developed to assist the surgeon in safely performing vitreoretinal surgeries; however, in the master-slave control, the robotic positioning accuracy depends on the surgeon's coordination skills. This paper proposes a new method of autonomous robotic positioning using the shadow of the surgical instrument. First, the microscope image is segmented into three regions - namely, a micropipette, its shadow, and the eye ground - using a Gaussian mixture model (GMM). The tips of the micropipette and its shadow are then extracted from the contour lines of the segmented regions. The micropipette is then autonomously moved down to the simulated eye ground until the distance between the tips of micropipette and its shadow in the microscopic image reaches a predefined threshold. To handle possible occlusions, the tip of the shadow is estimated using a Kalman filter. Experiments to evaluate the robotic positioning accuracy in the vertical direction were performed. The results show that the autonomous positioning using the Kalman filter enhanced the accuracy of robotic positioning.
AB - Vitreoretinal surgery is one of the most difficult surgical operations, even for experienced surgeons. Thus, a master-slave eye surgical robot has been developed to assist the surgeon in safely performing vitreoretinal surgeries; however, in the master-slave control, the robotic positioning accuracy depends on the surgeon's coordination skills. This paper proposes a new method of autonomous robotic positioning using the shadow of the surgical instrument. First, the microscope image is segmented into three regions - namely, a micropipette, its shadow, and the eye ground - using a Gaussian mixture model (GMM). The tips of the micropipette and its shadow are then extracted from the contour lines of the segmented regions. The micropipette is then autonomously moved down to the simulated eye ground until the distance between the tips of micropipette and its shadow in the microscopic image reaches a predefined threshold. To handle possible occlusions, the tip of the shadow is estimated using a Kalman filter. Experiments to evaluate the robotic positioning accuracy in the vertical direction were performed. The results show that the autonomous positioning using the Kalman filter enhanced the accuracy of robotic positioning.
UR - http://www.scopus.com/inward/record.url?scp=85056634501&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2018.8512633
DO - 10.1109/EMBC.2018.8512633
M3 - 会議への寄与
C2 - 30440727
AN - SCOPUS:85056634501
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1723
EP - 1726
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -