000			02437 am a22002413u 4500
042			_adc
100	1	0	_aLi, Lei _eauthor _91849
700	1	0	_aZimmer, Veronika A. _eauthor
700	1	0	_aSchnabel, Julia A. _eauthor
700	1	0	_aZhuang, Xiahai _eauthor _91852
245	0	0	_aMedical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review
260			_c2022-04-01.
500			_a/pmc/articles/PMC7614005/
500			_a/pubmed/35124370
520			_aLate gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars. The position and extent of LA scars provide important information on the pathophysiology and progression of atrial fibrillation (AF). Hence, LA LGE MRI computing and analysis are essential for computer-assisted diagnosis and treatment stratification of AF patients. Since manual delineations 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 as well as state-of-the-art results on public datasets. Finally, the possible future developments are outlined, with a brief survey on the potential clinical applications of the aforementioned methods. The review indicates that the research into this topic is still in the early stages. Although several methods have been proposed, especially for the LA cavity segmentation, there is still a large scope for further algorithmic developments due to performance issues related to the high variability of enhancement appearance and differences in image acquisition.
540			_a
540			_ahttps://creativecommons.org/licenses/by/4.0/This work is licensed under a CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) International license.
546			_aen
690			_aArticle
655	7		_aText _2local
786	0		_nMed Image Anal
856	4	1	_uhttp://dx.doi.org/10.1016/j.media.2022.102360 _zConnect to this object online.
999			_c1784 _d1784