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Real-time surgical needle detection using region-based convolutional neural networks

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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.


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.


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.


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.

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This work was funded by ImPACT Program of Council for Science, Technology and Innovation, Cabinet Office, Government of Japan, Grant-in-Aid for JSPS Research Fellows Number 18J12185 and Global Leader Program for Social Design and Management by the Ministry of Education, Culture, Sports, Science and Technology of Japan. We thank Prof. Nakatomi from the University of Tokyo Hospital for providing us with videos of microvascular anastomosis in real surgery.

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Correspondence to Atsushi Nakazawa.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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The clinical data were obtained under the regulations of the University of Tokyo.

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Nakazawa, A., Harada, K., Mitsuishi, M. et al. Real-time surgical needle detection using region-based convolutional neural networks. Int J CARS 15, 41–47 (2020).

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  • Needle detection
  • Region proposal
  • Convolutional neural network
  • Microsurgery