SynthesisMedical image analysis2022
Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review.
Synthesis in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
17 citing papers in PubMed, 69 citations in OpenAlex.
- CardioSynergyNet: A Closed-Loop Multi-Task Deep Learning Architecture for Cardiac Segmentation and Biomarkers with Diagnosis from Paired ED-ES Cine-MRI.Tomography (Ann Arbor, Mich.) · 2026Article
- Prognostic value of left atrial parameters derived from magnetic resonance in dilated cardiomyopathy: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2026Article
- Review
- mCardiacDx: Radar-Driven Contactless Monitoring and Diagnosis of Atrial Fibrillation.IEEE journal of translational engineering in health and medicine · 2026Article
- Assessment of Left Atrial Fibrosis With Cardiac MRI.Journal of magnetic resonance imaging : JMRI · 2025Article
- Deep learning-based cardiac computed tomography angiography left atrial segmentation and quantification in atrial fibrillation patients: a multi-model comparative study.Biomedical engineering online · 2025Article
- Automatic Bi-Atrial Segmentation and Biomarker Extraction from Late Gadolinium-Enhanced MRI Using Deep Learning.Expert systems with applications · 2025Article
- Investigating the Domain Adaptability of General-Purpose Foundation Models for Left Atrium Segmentation from MR Images.Functional imaging and modeling of the heart : ... International Workshop, FIMH ..., proceedings. FIMH (Conference) · 2025Article
- A semi-supervised domain adaptive medical image segmentation method based on dual-level multi-scale alignment.Scientific reports · 2025Article
- Quantification of atrial cardiomyopathy disease severity by electroanatomic voltage mapping and cardiac magnetic resonance imaging.Journal of cardiovascular electrophysiology · 2025Article
- Article
- Semi-supervised segmentation of cardiac chambers from LGE-CMR using feature consistency awareness.BMC cardiovascular disorders · 2024Article
- RAS Dataset: A 3D Cardiac LGE-MRI Dataset for Segmentation of Right Atrial Cavity.Scientific data · 2024Article
- Deep learning architecture for 3D image super-resolution of late gadolinium enhanced cardiac MRI.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Automatic Multiple Articulator Segmentation in Dynamic Speech MRI Using a Protocol Adaptive Stacked Transfer Learning U-NET Model.Bioengineering (Basel, Switzerland) · 2023Article
- Cardiac Magnetic Resonance Image Segmentation Method Based on Multi-Scale Feature Fusion and Sequence Relationship Learning.Sensors (Basel, Switzerland) · 2023Article
- LASSNet: A Four Steps Deep Neural Network for Left Atrial Segmentation and Scar Quantification.Left atrial and scar quantification and segmentation : first challenge, LAScarQS 2022 held in conjunction with MICCAI 2022, Singapore, September 18, 2022, proceedings · 2023Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 3 countries.
Funding
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 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.