Robust Real-Time Detection of Laparoscopic Instruments in Robot Surgery Using Convolutional Neural Networks with Motion Vector Prediction

More than half of post-operative complications can be prevented, and operation performances can be improved based on the feedback gathered from operations or notifications of the risks during operations in real time. However, existing surgical analysis methods are limited, because they involve time-...

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Bibliographic Details
Published in:Applied sciences 2019-07, Vol.9 (14), p.2865
Main Authors: Jo, Kyungmin, Choi, Yuna, Choi, Jaesoon, Chung, Jong Woo
Format: Article
Language:English
Subjects:
Algorithms
CNN
Complications
convolutional neural networks
Frames (data processing)
Frames per second
Laparoscopy
Medical instruments
Neural networks
Real time
robot surgery
Robotic surgery
Surgeons
Surgery
Surgical apparatus & instruments
Surgical instruments
tool detection
YOLO
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Summary:More than half of post-operative complications can be prevented, and operation performances can be improved based on the feedback gathered from operations or notifications of the risks during operations in real time. However, existing surgical analysis methods are limited, because they involve time-consuming processes and subjective opinions. Therefore, the detection of surgical instruments is necessary for (a) conducting objective analyses, or (b) providing risk notifications associated with a surgical procedure in real time. We propose a new real-time detection algorithm for detection of surgical instruments using convolutional neural networks (CNNs). This algorithm is based on an object detection system YOLO9000 and ensures continuity of detection of the surgical tools in successive imaging frames based on motion vector prediction. This method exhibits a constant performance irrespective of a surgical instrument class, while the mean average precision (mAP) of all the tools is 84.7, with a speed of 38 frames per second (FPS).
ISSN:2076-3417
2076-3417
DOI:10.3390/app9142865