Mini Review: Deep Learning for Atrial Segmentation From Late Gadolinium-Enhanced MRIs

Frontiers in Cardiovascular Medicine

article

Kevin Jamart¹  Zhaohan Xiong¹  Gonzalo D. Maso Talou¹  Martin K. Stiles²  Jichao Zhao¹ 

[1] Auckland Bioengineering Institute, The University of Auckland;Waikato Clinical School, Faculty of Medical and Health Sciences, The University of Auckland

关键词: atrial fibrillation;    left atrium;    machine learning;    image segmentation;    convolutional neural network;    LGE-MRI;   

DOI  :  10.3389/fcvm.2020.00086

学科分类：地球科学(综合)

来源: Frontiers

【 摘 要 】

Segmentation and 3D reconstruction of the human atria is of crucial importance for precise diagnosis and treatment of atrial fibrillation, the most common cardiac arrhythmia. However, the current manual segmentation of the atria from medical images is a time-consuming, labor-intensive, and error-prone process. The recent emergence of artificial intelligence, particularly deep learning, provides an alternative solution to the traditional methods that fail to accurately segment atrial structures from clinical images. This has been illustrated during the recent 2018 Atrial Segmentation Challenge for which most of the challengers developed deep learning approaches for atrial segmentation, reaching high accuracy (>90% Dice score). However, as significant discrepancies exist between the approaches developed, many important questions remain unanswered, such as which deep learning architectures and methods to ensure reliability while achieving the best performance. In this paper, we conduct an in-depth review of the current state-of-the-art of deep learning approaches for atrial segmentation from late gadolinium-enhanced MRIs, and provide critical insights for overcoming the main hindrances faced in this task.

【 授权许可】

CC BY