Atsushi Nakazawa1, Kanako Harada2, Mamoru Mitsuishi2, Pierre Jannin3. 1. Department of Mechanical Engineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan. a.nakazawa@nml.t.u-tokyo.ac.jp. 2. Department of Mechanical Engineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan. 3. Univ Rennes, INSERM, LTSI - UMR 1099, 35000, Rennes, France.
Abstract
OBJECTIVE: Conventional surgical assistance and skill analysis for suturing mostly focus on the motions of the tools. As the quality of the suturing is determined by needle motions relative to the tissues, having knowledge of the needle motion would be useful for surgical assistance and skill analysis. As the first step toward demonstrating the usefulness of the knowledge of the needle motion, we developed a needle detection algorithm. METHODS: Owing to the small needle size, attaching sensors to it is difficult. Therefore, we developed a real-time video-based needle detection algorithm using a region-based convolutional neural network. RESULTS: Our method successfully detected the needle with an average precision of 89.2%. The needle was robustly detected even when the needle was heavily occluded by the tools and/or the blood vessels during microvascular anastomosis. However, there were some incorrect detections, including partial detection. CONCLUSION: To the best of our knowledge, this is the first time deep neural networks have been applied to real-time needle detection. In the future, we will develop a needle pose estimation algorithm using the predicted needle location toward computer-aided surgical assistance and surgical skill analysis.
OBJECTIVE: Conventional surgical assistance and skill analysis for suturing mostly focus on the motions of the tools. As the quality of the suturing is determined by needle motions relative to the tissues, having knowledge of the needle motion would be useful for surgical assistance and skill analysis. As the first step toward demonstrating the usefulness of the knowledge of the needle motion, we developed a needle detection algorithm. METHODS: Owing to the small needle size, attaching sensors to it is difficult. Therefore, we developed a real-time video-based needle detection algorithm using a region-based convolutional neural network. RESULTS: Our method successfully detected the needle with an average precision of 89.2%. The needle was robustly detected even when the needle was heavily occluded by the tools and/or the blood vessels during microvascular anastomosis. However, there were some incorrect detections, including partial detection. CONCLUSION: To the best of our knowledge, this is the first time deep neural networks have been applied to real-time needle detection. In the future, we will develop a needle pose estimation algorithm using the predicted needle location toward computer-aided surgical assistance and surgical skill analysis.
Keywords:
Convolutional neural network; Microsurgery; Needle detection; Region proposal
Authors: Mahtab J Fard; Sattar Ameri; R Darin Ellis; Ratna B Chinnam; Abhilash K Pandya; Michael D Klein Journal: Int J Med Robot Date: 2017-06-29 Impact factor: 2.547
Authors: Will Pryor; Yotam Barnoy; Suraj Raval; Xiaolong Liu; Lamar Mair; Daniel Lerner; Onder Erin; Gregory D Hager; Yancy Diaz-Mercado; Axel Krieger Journal: Rep U S Date: 2021-12-16