  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.
