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Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks
Five billion people in the world lack access to quality surgical care. S...
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Video-based surgical skill assessment using 3D convolutional neural networks
Purpose: A profound education of novice surgeons is crucial to ensure th...
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Automatic Tool Landmark Detection for Stereo Vision in Robot-Assisted Retinal Surgery
Computer vision and robotics are being increasingly applied in medical i...
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Using Computer Vision to Automate Hand Detection and Tracking of Surgeon Movements in Videos of Open Surgery
Open, or non-laparoscopic surgery, represents the vast majority of all o...
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Who's Better, Who's Best: Skill Determination in Video using Deep Ranking
This paper presents a method for assessing skill of performance from vid...
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Multi-stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning
In robotic surgery, task automation and learning from demonstration comb...
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Assessing fish abundance from underwater video using deep neural networks
Uses of underwater videos to assess diversity and abundance of fish are ...
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Detection and Localization of Robotic Tools in Robot-Assisted Surgery Videos Using Deep Neural Networks for Region Proposal and Detection
Video understanding of robot-assisted surgery (RAS) videos is an active research area. Modeling the gestures and skill level of surgeons presents an interesting problem. The insights drawn may be applied in effective skill acquisition, objective skill assessment, real-time feedback, and human-robot collaborative surgeries. We propose a solution to the tool detection and localization open problem in RAS video understanding, using a strictly computer vision approach and the recent advances of deep learning. We propose an architecture using multimodal convolutional neural networks for fast detection and localization of tools in RAS videos. To our knowledge, this approach will be the first to incorporate deep neural networks for tool detection and localization in RAS videos. Our architecture applies a Region Proposal Network (RPN), and a multi-modal two stream convolutional network for object detection, to jointly predict objectness and localization on a fusion of image and temporal motion cues. Our results with an Average Precision (AP) of 91 mean computation time of 0.1 seconds per test frame detection indicate that our study is superior to conventionally used methods for medical imaging while also emphasizing the benefits of using RPN for precision and efficiency. We also introduce a new dataset, ATLAS Dione, for RAS video understanding. Our dataset provides video data of ten surgeons from Roswell Park Cancer Institute (RPCI) (Buffalo, NY) performing six different surgical tasks on the daVinci Surgical System (dVSS R ) with annotations of robotic tools per frame.
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