Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review

TL;DR

This review paper summarizes current computational methods for analyzing LGE MRI images of the left atrium in atrial fibrillation, highlighting challenges and future directions for automated scar segmentation and quantification.

Contribution

It provides a comprehensive overview of existing techniques for LA segmentation and discusses validation strategies and clinical applications, emphasizing the early stage of research in this area.

Findings

Several methods proposed for LA segmentation.

Performance issues due to variability in image appearance.

Large scope for future algorithmic improvements.

Abstract

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of scars provide important information of the pathophysiology and progression of atrial fibrillation (AF). Hence, LA scar segmentation and quantification from LGE MRI can be useful in computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineation can be time-consuming and subject to intra- and inter-expert variability, automating this computing is highly desired, which nevertheless is still challenging and under-researched. This paper aims to provide a systematic review on computing methods for LA cavity, wall, scar and ablation gap segmentation and quantification from LGE MRI, and the related literature for AF studies. Specifically, we first summarize AF-related imaging techniques, particularly LGE MRI. Then, we review the methodologies of the four computing tasks in detail, and summarize the validation strategies applied in each task. Finally, the possible future developments are outlined, with a brief survey on the potential clinical applications of the aforementioned methods. The review shows that the research into this topic is still in early stages. Although several methods have been proposed, especially for LA segmentation, there is still large scope for further algorithmic developments due to performance issues related to the high variability of enhancement appearance and differences in image acquisition.