ArticleLeft atrial and scar quantification and segmentation : first challenge, LAScarQS 2022 held in conjunction with MICCAI 2022, Singapore, September 18, 2022, proceedings2023
LASSNet: A Four Steps Deep Neural Network for Left Atrial Segmentation and Scar Quantification.
Article in Left atrial and scar quantification and segmentation : first challenge, LAScarQS 2022 held in conjunction with MICCAI 2022, Singapore, September 18, 2022, proceedings, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 14 citations in OpenAlex.
- Transmural heterogeneity in 3D fibrosis distribution is associated with endo-epicardial electrical dyssynchrony in persistent atrial fibrillation.The Journal of physiology · 2026Article
- A computational pipeline for clustering left atrial appendage morphology via elastic shape analysis.ArXiv · 2025Article
- Multi-Scale Feature Fusion with Image-Driven Spatial Integration for Left Atrium Segmentation from Cardiac MR Images.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- Assessing the arrhythmogenic propensity of fibrotic substrate using digital twins to inform a mechanisms-based atrial fibrillation ablation strategy.Nature cardiovascular research · 2024Article
- Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.Physiological reviews · 2024Review
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
Abstract
Accurate quantification of left atrium (LA) scar in patients with atrial fibrillation is essential to guide successful ablation strategies. Prior to LA scar quantification, a proper LA cavity segmentation is required to ensure exact location of scar. Both tasks can be extremely time-consuming and are subject to inter-observer disagreements when done manually. We developed and validated a deep neural network to automatically segment the LA cavity and the LA scar. The global architecture uses a multi-network sequential approach in two stages which segment the LA cavity and the LA Scar. Each stage has two steps: a region of interest Neural Network and a refined segmentation network. We analysed the performances of our network according to different parameters and applied data triaging. 200+ late gadolinium enhancement magnetic resonance images were provided by the LAScarQS 2022 Challenge. Finally, we compared our performances for scar quantification to the literature and demonstrated improved performances.
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Registered trials
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