Real-time surgical needle detection using region-based convolutional neural networks
Real-time surgical needle detection using region-based convolutional neural networks
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DOI：
10.1007/s11548-019-02050-9
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发表时间：
2020-01-01
影响因子：
3
通讯作者：
Jannin, Pierre
中科院分区：
文献类型：
--
作者：
Nakazawa, Atsushi;Harada, Kanako;Jannin, Pierre
目的传统的脊柱侧凸手术辅助和技术分析主要集中在工具的运动上。由于缝合质量由针相对于组织的运动决定，因此了解针运动对于手术辅助和技能分析将是有用的。作为证明针运动知识的有用性的第一步，我们开发了针检测算法。方法由于针头尺寸小，传感器难以附着在其上。因此，我们开发了一个实时的基于视频的针检测算法，使用基于区域的卷积神经网络。结果该方法成功地检测出了针头，平均准确率为89.2%。即使在微血管吻合术期间针头被工具和/或血管严重堵塞时，也能稳健地检测到针头。然而，也有一些不正确的检测，包括部分检测。据我们所知，这是深度神经网络首次应用于实时针头检测。在未来，我们将开发一个针姿态估计算法，使用预测针的位置对计算机辅助手术辅助和手术技能分析。
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.