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

• 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
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app9142865

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).