Department of Biomedical Engineering, Seoul National University, Seoul, Republic of Korea.
Biomedical Engineering Research Center, Department of Convergence Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
J Healthc Eng. 2018 May 2;2018:8079713. doi: 10.1155/2018/8079713. eCollection 2018.
Although the use of the surgical robot is rapidly expanding for various medical treatments, there still exist safety issues and concerns about robot-assisted surgeries due to limited vision through a laparoscope, which may cause compromised situation awareness and surgical errors requiring rapid emergency conversion to open surgery. To assist surgeon's situation awareness and preventive emergency response, this study proposes situation information guidance through a vision-based common algorithm architecture for automatic detection and tracking of intraoperative hemorrhage and surgical instruments. The proposed common architecture comprises the location of the object of interest using feature texture, morphological information, and the tracking of the object based on Kalman filter for robustness with reduced error. The average recall and precision of the instrument detection in four prostate surgery videos were 96% and 86%, and the accuracy of the hemorrhage detection in two prostate surgery videos was 98%. Results demonstrate the robustness of the automatic intraoperative object detection and tracking which can be used to enhance the surgeon's preventive state recognition during robot-assisted surgery.
尽管手术机器人在各种医疗治疗中的应用迅速扩大，但由于腹腔镜的有限视野，机器人辅助手术仍然存在安全问题和担忧，这可能导致情况意识受损和手术错误，需要迅速紧急转为开放手术。为了帮助外科医生提高情况意识和进行预防性应急响应，本研究提出了一种基于视觉的通用算法架构的情况信息指导，用于自动检测和跟踪术中出血和手术器械。所提出的通用架构包括使用特征纹理、形态学信息的感兴趣对象的位置，以及基于卡尔曼滤波器的对象跟踪，以实现鲁棒性和减少误差。在四个前列腺手术视频中，器械检测的平均召回率和精度分别为 96%和 86%，在两个前列腺手术视频中，出血检测的准确率为 98%。结果表明，自动术中物体检测和跟踪具有鲁棒性，可以用于增强机器人辅助手术中外科医生的预防性状态识别。
