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.

患有心房颤动的患者具有罹患缺血性中风的风险增加了 5-7 倍。本研究通过量化形态学特征并建立统计形状模型，展示了左心耳形状可以通过 5 个主成分分析模式进行量化，并且能够识别出所谓的鸡翅和非鸡翅形态的两个不同形状群集。

基于有符号距离场的左房耳分割和统计形状建模

Signed Distance Field based Segmentation and Statistical Shape Modelling  of the Left Atrial Appendage

The assessment of left atrial appendage (LAA) thrombogenesis has experienced
major advances with the adoption of patient-specific computational fluid
dynamics (CFD) simulations. Nonetheless, due to the vast computational
resources and long execution times required by fluid dynamics solvers, there is
an ever-growing body of work aiming to develop surrogate models of fluid flow
simulations based on neural networks. The present study builds on this
foundation by developing a deep learning (DL) framework capable of predicting
the endothelial cell activation potential (ECAP), linked to the risk of
thrombosis, solely from the patient-specific LAA geometry. To this end, we
leveraged recent advancements in Geometric DL, which seamlessly extend the
unparalleled potential of convolutional neural networks (CNN), to non-Euclidean
data such as meshes. The model was trained with a dataset combining 202
synthetic and 54 real LAA, predicting the ECAP distributions instantaneously,
with an average mean absolute error of 0.563. Moreover, the resulting framework
manages to predict the anatomical features related to higher ECAP values even
when trained exclusively on synthetic cases.

本研究通过开发深度学习 (DL) 框架，仅根据患者的特定左心房附属物（LAA）几何形状来预测与血栓形成风险相关的内皮细胞活化潜力（ECAP）分布，建立在几何 DL 的最新进展基础上，该模型在综合合成和 54 个真实的 LAA 数据集上进行训练，平均平均绝对误差为 0.563。