Nguyen D L H, Garreau M, Auffret V, Le Breton H, Verhoye J P, Haigron P
Annu Int Conf IEEE Eng Med Biol Soc. 2013;2013:4378-81. doi: 10.1109/EMBC.2013.6610516.
The main objective of this work is to track the aortic valve plane in intra-operative fluoroscopic images in order to optimize and secure Transcatheter Aortic Valve Implantation (TAVI) procedure. This paper is focused on the issue of aortic valve calcifications tracking in fluoroscopic images. We propose a new method based on the Tracking-Learning-Detection approach, applied to the aortic valve calcifications in order to determine the position of the aortic valve plane in intra-operative TAVI images. This main contribution concerns the improvement of object detection by updating the recursive tracker in which all features are tracked jointly. The approach has been evaluated on four patient databases, providing an absolute mean displacement error less than 10 pixels (≈2mm). Its suitability for the TAVI procedure has been analyzed.
这项工作的主要目标是在术中透视图像中追踪主动脉瓣平面，以优化并确保经导管主动脉瓣植入术（TAVI）的顺利进行。本文聚焦于透视图像中主动脉瓣钙化的追踪问题。我们提出了一种基于跟踪-学习-检测方法的新方法，将其应用于主动脉瓣钙化，以确定术中TAVI图像中主动脉瓣平面的位置。这一主要贡献在于通过更新递归跟踪器来改进目标检测，在该跟踪器中所有特征被联合跟踪。该方法已在四个患者数据库上进行了评估，绝对平均位移误差小于10像素（约2毫米）。同时分析了其在TAVI手术中的适用性。
