Mitral valve repair is a very difficult operation, often requiring experienced surgeons. The doctor will insert a prosthetic ring to aid in the restoration of heart function. The location of the prosthesis' sutures is critical. Obtaining and studying them during the procedure is a valuable learning experience for new surgeons. This paper proposes a landmark detection network for detecting sutures in endoscopic pictures, which solves the problem of a variable number of suture points in the images. Because there are two datasets, one from the simulated domain and the other from real intraoperative data, this work uses cycleGAN to interconvert the images from the two domains to obtain a larger dataset and a better score on real intraoperative data. This paper performed the tests using a simulated dataset of 2708 photos and a real dataset of 2376 images. The mean sensitivity on the simulated dataset is about 75.64% and the precision is about 73.62%. The mean sensitivity on the real dataset is about 50.23% and the precision is about 62.76%. The data is from the AdaptOR MICCAI Challenge 2021, which can be found at https://zenodo.org/record/4646979\#.YO1zLUxCQ2x.
翻译:Mitral 阀门修理是一项非常困难的操作,通常需要有经验的外科医生。 医生将插入一个假肢环, 以帮助恢复心脏功能。 假肢的缝线的位置至关重要。 程序期间获取和研究这些缝线是新外科医生的宝贵学习经验。 本文建议建立一个里程碑式的检测网络, 用于检测内窥镜中的缝线, 解决图像中可变的缝合点数问题。 由于有两个数据集, 一个来自模拟域, 另一个来自真正的内科数据, 这项工作使用循环GAN将两个域的图像相互转换, 以获得更大的数据集和更好的实际内科数据评分。 本文使用一个模拟数据集, 包括2708张照片和2376图像的真数据集进行了测试。 模拟数据集的平均灵敏度约为75.64%, 精确度约为73.62%。 真实数据集的平均灵敏度约为50.23 %, 精确度约为62.76%。 数据来自调制的MICCAI- 261/L. Q. 挑战2021/ Q. 数据, https. http:// https. http:// https. https