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

TL;DR

This paper introduces an automatic pipeline that uses deep learning to segment the left atrial appendage from CT images, creating smooth 3D models for shape analysis and classification related to stroke risk.

Contribution

It presents a novel method combining signed distance fields and deep learning for accurate, smooth LAA segmentation and statistical shape modeling from CT images.

Findings

Successfully segmented 106 LAAs automatically

Identified two main shape clusters corresponding to known morphologies

Achieved shape quantification with approximately 5 PCA modes

Abstract

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.