ArticleFuture generations computer systems : FGCS2020
Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention.
Article in Future generations computer systems : FGCS, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 101 citations in OpenAlex.
- Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review.Medical image analysis · 2022Pooled it
- SSMSNet: Scribble-Supervised Myocardial Scar Segmentation in Late Gadolinium Enhancement Images.Diagnostics (Basel, Switzerland) · 2026Article
- A paradigm shift toward full-cycle management of atrial fibrillation: integrating digital twins and artificial intelligence.Frontiers in cardiovascular medicine · 2026Review
- Robust myocardium detection and scar severity classification in LGE-CMR using ScarYOLO and contrastive learning.European journal of medical research · 2025Article
- Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review.Journal of medical Internet research · 2025Article
- A novel network with enhanced edge information for left atrium segmentation from LGE-MRI.Frontiers in physiology · 2024Article
- JOINT DEEP LEARNING FOR IMPROVED MYOCARDIAL SCAR DETECTION FROM CARDIAC MRI.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
- Utilisation of deep learning for COVID-19 diagnosis.Clinical radiology · 2023Review
- 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
- A Hybrid Method for Customer Segmentation in Saudi Arabia Restaurants Using Clustering, Neural Networks and Optimization Learning Techniques.Arabian journal for science and engineering · 2023Article
- Automatic 3D Surface Reconstruction of the Left Atrium From Clinically Mapped Point Clouds Using Convolutional Neural Networks.Frontiers in physiology · 2022Article
- Direct left-ventricular global longitudinal strain (GLS) computation with a fully convolutional network.Journal of biomechanics · 2022Article
- Two-Stage Segmentation Framework Based on Distance Transformation.Sensors (Basel, Switzerland) · 2021Article
- Automatic Meniscus Segmentation Using Adversarial Learning-Based Segmentation Network with Object-Aware Map in Knee MR Images.Diagnostics (Basel, Switzerland) · 2021Article
- TransMed: Transformers Advance Multi-Modal Medical Image Classification.Diagnostics (Basel, Switzerland) · 2021Article
- A Deep Learning Approach for Segmentation, Classification, and Visualization of 3-D High-Frequency Ultrasound Images of Mouse Embryos.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2021Article
- Fast and Automated Segmentation for the Three-Directional Multi-Slice Cine Myocardial Velocity Mapping.Diagnostics (Basel, Switzerland) · 2021Article
- Deep learning for the fully automated segmentation of the inner ear on MRI.Scientific reports · 2021Observational
- Feasibility of using a novel automatic cardiac segmentation algorithm in the clinical routine of lung cancer patients.PloS one · 2021Article
- Clinical Analysis of Improved Particle Swarm Algorithm-Based Magnetic Resonance Imaging Diagnosis of Placenta Accreta.Contrast media & molecular imaging · 2021Article
Corrections and comments
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Authors and funding
16 authors at 7 institutions in 3 countries.
Funding
Abstract
Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3D LGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (
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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.