Accurate 3D Multi-marker Tracking in X-ray Cardiac Sequences Using a Two-Stage Graph Modeling Approach

Accurate 3D Multi-marker Tracking in X-ray Cardiac Sequences Using a Two-Stage Graph Modeling Approach
复制标题

DOI:
10.1007/978-3-642-40246-3_15
复制
发表时间:
2013-08
期刊:
2016 6th International Conference on System Engineering and Technology (ICSET)
影响因子:
--
通讯作者:
Xiaoyan Jiang;D. Haase;Marco Körner;W. Bothe;Joachim Denzler
Xiaoyan Jiang;D. Haase;Marco Körner;W. Bothe;Joachim Denzler
中科院分区:
其他
文献类型:
--
作者:
Xiaoyan Jiang;D. Haase;Marco Körner;W. Bothe;Joachim Denzler

文献摘要

被引文献

相似文献

深入分析心脏在不同条件下的运动是心脏外科的重要问题。为了揭示相关肌肉部分的运动,采集植入的不透射线标记物的双平面X射线记录。由于在图像中手动定位这些标记是一项非常耗时的任务,我们的目标是自动化这一过程。考虑到在记录的数据,如丢失检测或2D闭塞的困难,我们提出了一个两阶段的基于图的方法为3D轨迹和3D轨迹生成。在我们的方法的第一阶段,我们构建了一个有向的非循环图的3D观测,通过最短路径优化获得轨迹。之后,以类似的方式从轨迹片段图中提取完整轨迹。这导致了检测和轨迹的全局最优链接,同时提供了一个灵活的框架,可以很容易地适应各种跟踪场景的基础上的边缘成本函数。我们验证了我们的方法上的X射线序列的跳动的羊心脏手动标记地面真理标记位置的基础上。结果表明,我们的方法的性能与人类专家相当,而标准的3D跟踪方法(如粒子滤波器)则优于人类专家。
The in-depth analysis of heart movements under varying conditions is an important problem of cardiac surgery. To reveal the movement of relevant muscular parts, biplanar X-ray recordings of implanted radio-opaque markers are acquired. As manually locating these markers in the images is a very time-consuming task, our goal is to automate this process. Taking into account the difficulties in the recorded data such as missing detections or 2D occlusions, we propose a two-stage graph-based approach for both 3D tracklet and 3D track generation. In the first stage of our approach, we construct a directed acyclic graph of 3D observations to obtain tracklets via shortest path optimization. Afterwards, full tracks are extracted from a tracklet graph in a similar manner. This results in a globally optimal linking of detections and tracklets, while providing a flexible framework which can easily be adapted to various tracking scenarios based on the edge cost functions. We validate our approach on an X-ray sequence of a beating sheep heart based on manually labeled ground-truth marker positions. The results show that the performance of our method is comparable to human experts, while standard 3D tracking approaches such as particle filters are outperformed.