ArticleInternational journal of computer assisted radiology and surgery2020
Real-time surgical needle detection using region-based convolutional neural networks.
Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Bibliometric analysis of deep learning for surgical instrument segmentation, detection and tracking in minimally invasive surgery.Frontiers in digital health · 2026Review
- Monocular suture needle pose detection using synthetic data augmented convolutional neural network.International journal of computer assisted radiology and surgery · 2025Article
- Artificial Intelligence in Microsurgical Planning: A Five-Year Leap in Clinical Translation.Journal of clinical medicine · 2025Review
- Decoding the Impact of AI on Microsurgery: Systematic Review and Classification of Six Subdomains for Future Development.Plastic and reconstructive surgery. Global open · 2024Article
- A Systematic Review of Artificial Intelligence Applications in Plastic Surgery: Looking to the Future.Plastic and reconstructive surgery. Global open · 2022Article
- Localization and Control of Magnetic Suture Needles in Cluttered Surgical Site with Blood and Tissue.Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and SystemsArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
objectiveConventional 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.
methodsOwing 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.
resultsOur 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.
conclusionTo 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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