A Survey of Left Atrial Appendage Segmentation and Analysis in 3D and 4D Medical Images

TL;DR

This survey reviews automatic segmentation methods of the left atrial appendage in 3D and 4D medical images, highlighting their classifications, effectiveness, challenges, and future directions to improve stroke risk assessment and procedural planning.

Contribution

It provides a comprehensive classification, comparison, and evaluation of existing automatic LAA segmentation methods in 3D and 4D medical imaging.

Findings

Semi- and fully-automatic methods vary in accuracy and efficiency.

Current challenges include image quality and variability in anatomy.

Future approaches may involve deep learning techniques.

Abstract

Atrial fibrillation (AF) is a cardiovascular disease identified as one of the main risk factors for stroke. The majority of strokes due to AF are caused by clots originating in the left atrial appendage (LAA). LAA occlusion is an effective procedure for reducing stroke risk. Planning the procedure using pre-procedural imaging and analysis has shown benefits. The analysis is commonly done by manually segmenting the appendage on 2D slices. Automatic LAA segmentation methods could save an expert's time and provide insightful 3D visualizations and accurate automatic measurements to aid in medical procedures. Several semi- and fully-automatic methods for segmenting the appendage have been proposed. This paper provides a review of automatic LAA segmentation methods on 3D and 4D medical images, including CT, MRI, and echocardiogram images. We classify methods into heuristic and model-based methods, as well as into semi- and fully-automatic methods. We summarize and compare the proposed methods, evaluate their effectiveness, and present current challenges in the field and approaches to overcome them.