Patients with atrial fibrillation have a 5-7 fold increased risk of having an ischemic stroke. In these cases, the most common site of thrombus localization is inside the left atrial appendage (LAA) and studies have shown a correlation between the LAA shape and the risk of ischemic stroke. These studies make use of manual measurement and qualitative assessment of shape and are therefore prone to large inter-observer discrepancies, which may explain the contradictions between the conclusions in different studies. We argue that quantitative shape descriptors are necessary to robustly characterize LAA morphology and relate to other functional parameters and stroke risk. Deep Learning methods are becoming standardly available for segmenting cardiovascular structures from high resolution images such as computed tomography (CT), but only few have been tested for LAA segmentation. Furthermore, the majority of segmentation algorithms produces non-smooth 3D models that are not ideal for further processing, such as statistical shape analysis or computational fluid modelling. In this paper we present a fully automatic pipeline for image segmentation, mesh model creation and statistical shape modelling of the LAA. The LAA anatomy is implicitly represented as a signed distance field (SDF), which is directly regressed from the CT image using Deep Learning. The SDF is further used for registering the LAA shapes to a common template and build a statistical shape model (SSM). Based on 106 automatically segmented LAAs, the built SSM reveals that the LAA shape can be quantified using approximately 5 PCA modes and allows the identification of two distinct shape clusters corresponding to the so-called chicken-wing and non-chicken-wing morphologies.

翻译：房颤患者发生缺血性卒中的风险增加5-7倍。在这些病例中，血栓最常形成的部位位于左心耳（LAA）内，研究表明LAA形态与缺血性卒中风险存在相关性。然而，现有研究多依赖人工测量和定性形态评估，导致观察者间差异较大，这可能解释不同研究结论间的矛盾。我们认为需要定量形态描述符来稳健表征LAA形态特征，并将其与其它功能参数及卒中风险相关联。深度学习技术已成为从计算机断层扫描（CT）等高分辨率图像中分割心血管结构的标准方法，但仅有少数研究测试其在LAA分割中的应用。此外，多数分割算法生成的非光滑三维模型难以满足统计形态分析或计算流体力学建模等后续处理要求。本文提出一种全自动流水线，用于LAA的图像分割、网格模型生成和统计形状建模。LAA解剖结构以有符号距离场（SDF）形式隐式表示，通过深度学习直接从CT图像回归得到。SDF进一步用于将LAA形状配准至通用模板，并构建统计形状模型（SSM）。基于106例自动分割的LAA数据，所构建的SSM表明LAA形态可通过约5个PCA模式量化表征，并识别出对应"鸡翅型"与"非鸡翅型"两种形态的两类形状簇。