Abstract
In preoperative planning of left atrial appendage closure (LAAC) with CT angiography, the assessment of the appendage orifice plays a crucial role in choosing an appropriate LAAC device size and a proper C-arm angulation. However, accurate orifice detection is laborious because of the high anatomic variation of the appendage, as well as the unclear orifice position and orientation in the available views. We propose an automatic orifice detection approach performing a search on the principal medial axis of the appendage, where we present an efficient iterative algorithm to grow the axis from the appendage to the left atrium. We propose to use the axis-to-surface distance of the appendage for efficient and effective detection. To localize the necessary initial seed for growing the medial axis, we train an artificial localization agent using an actor-critic reinforcement learning approach, defining the localization as a sequential decision process. The entire detection process takes only about 8 seconds, and the variance of the detected orifice with respect to annotations from two experts is calculated to be significantly small and less than the inter-observer variance. The proposed orifice search on the medial axis of the appendage comparing only its distance from the surface provides a simple, yet robust solution for orifice detection. While being the first fully automatic approach and providing a detection error below the inter-observer difference, our method improved the detection efficiency by eighteen times compared to the existing solution, therefore, can be potentially useful for physicians.
Abstract (translated)
在CT血管造影术前左心耳关闭术（LAAC）的计划中，阑尾孔的评估对于选择合适的LAAC装置尺寸和合适的C臂角度起着至关重要的作用。然而，精确的孔检测是困难的，因为高解剖变异的附属物，以及孔的位置和方向不明，在现有的视图。我们提出了一种在阑尾主中轴线上进行搜索的自动孔板检测方法，在该方法中，我们提出了一种有效的迭代算法，将阑尾轴线扩展到左心房。我们建议利用附属物的轴-面距离进行有效的检测。为了定位生长中轴线所需的初始种子，我们使用行为批评家强化学习方法训练了一种人工定位剂，将定位定义为一个连续的决策过程。整个检测过程仅需8秒左右，计算出被检测孔板相对于两位专家标注的方差非常小，小于观察者间方差。在附件中轴上仅比较其与表面之间的距离的拟议孔搜索为孔检测提供了一个简单而强大的解决方案。虽然我们的方法是第一个完全自动的方法，并且在观察者之间的差异之下提供了一个检测错误，但是与现有的解决方案相比，我们的方法将检测效率提高了18倍，因此，对医生可能有用。
URL
https://arxiv.org/abs/1904.01241
