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Left atrial appendage (LAA) closure (LAAC) is a minimally invasiveimplant-based method to prevent cardiovascular stroke in patients withnon-valvular atrial fibrillation. Assessing the LAA orifice in preoperative CTangiography plays a crucial role in choosing an appropriate LAAC implant sizeand a proper C-arm angulation. However, accurate orifice localization is hardbecause of the high anatomic variation of LAA, and unclear position andorientation of the orifice in available CT views. Deep localization models alsoyield high error in localizing the orifice in CT image because of the tinystructure of orifice compared to the vastness of CT image. In this paper, wepropose a centerline depth-based reinforcement learning (RL) world foreffective orifice localization in a small search space. In our scheme, an RLagent observes the centerline-to-surface distance and navigates through the LAAcenterline to localize the orifice. Thus, the search space is significantlyreduced facilitating improved localization. The proposed formulation couldresult in high localization accuracy comparing to the expert-annotations in 98CT images. Moreover, the localization process takes about 8 seconds which is 18times more efficient than the existing method. Therefore, this can be a usefulaid to physicians during the preprocedural planning of LAAC.

Centerline Depth World Reinforcement Learning-based Left Atrial Appendage Orifice Localization