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Three‐dimensional posture estimation of robot forceps using endoscope with convolutional neural network
Author(s) -
Mikada Takuto,
Kanno Takahiro,
Kawase Toshihiro,
Miyazaki Tetsuro,
Kawashima Kenji
Publication year - 2020
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.2062
Subject(s) - forceps , artificial intelligence , computer vision , convolutional neural network , computer science , robot , kinematics , image (mathematics) , medicine , surgery , physics , classical mechanics
Background In recent years, there has been significant developments in surgical robots. Image‐based sensing of surgical instruments, without the use of electric sensors, are preferred for easily washable robots. Methods We propose a method to estimate the three‐dimensional posture of the tip of the forceps tip by using an endoscopic image. A convolutional neural network (CNN) receives the image of the tracked markers attached to the forceps as an input and outputs the posture of the forceps. Results The posture estimation results showed that the posture estimated from the image followed the electrical sensor. The estimated results of the external force calculated based on the posture also followed the measured values. Conclusion The method which estimates the forceps posture from the image using CNN is effective. The mean absolute error of the estimated external force is smaller than the human detection limit.