ArticleCardiovascular digital health journal2022
Anatomically informed deep learning on contrast-enhanced cardiac magnetic resonance imaging for scar segmentation and clinical feature extraction.
Article in Cardiovascular digital health journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01076660 (Left Ventricular Structural Predictors of Sudden Cardiac Death [Substudy of), which is not on this map. Cited by 20 papers.
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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.
Left Ventricular Structural Predictors of Sudden Cardiac Death [Substudy of: Functional Energetics and Imaging for Phenotypic Characterization of Patients at Risk for Sudden Cardiac Death, See Also NCT000181233]
Who cites it
20 citing papers in PubMed.
- A pipeline for developing deep learning prognostic prediction models in cardiac magnetic resonance image analysis.European heart journal. Digital health · 2026Article
- Review
- AI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review.Journal of cardiovascular development and disease · 2025Review
- Article
- Deep learning pipeline for fully automated myocardial infarct segmentation from clinical cardiac MR scans.Radiology advances · 2025Article
- Multimodal AI to forecast arrhythmic death in hypertrophic cardiomyopathy.Nature cardiovascular research · 2025Article
- Automated fibrosis segmentation from wideband post-contrastMagnetic resonance in medicine · 2025Article
- Cardiac magnetic resonance research advances in myocardial fibrosis of hypertrophic cardiomyopathy.Frontiers in cardiovascular medicine · 2025Review
- Progress in the Clinical Application of Artificial Intelligence for Left Ventricle Analysis in Cardiac Magnetic Resonance.Reviews in cardiovascular medicine · 2024Review
- The beating heart: artificial intelligence for cardiovascular application in the clinic.Magma (New York, N.Y.) · 2024Review
- Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference.IEEE transactions on medical imaging · 2024Article
- Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation.Physiological reviews · 2024Review
- Cardiac Fibrosis Automated Diagnosis Based on FibrosisNet Network Using CMR Ischemic Cardiomyopathy.Diagnostics (Basel, Switzerland) · 2024Article
- Twenty-five years of research in cardiac imaging in electrophysiology procedures for atrial and ventricular arrhythmias.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2023Article
- Diagnostic performance of deep learning-based vessel extraction and stenosis detection on coronary computed tomography angiography for coronary artery disease: a multi-reader multi-case study.La Radiologia medica · 2023Article
- Interpretable machine learning for automated left ventricular scar quantification in hypertrophic cardiomyopathy patients.PLOS digital health · 2023Article
- On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks.Archives of computational methods in engineering : state of the art reviews · 2023Article
- Arrhythmic sudden death survival prediction using deep learning analysis of scarring in the heart.Nature cardiovascular research · 2022Article
- Advanced imaging for risk stratification for ventricular arrhythmias and sudden cardiac death.Frontiers in cardiovascular medicine · 2022Review
- Diagnostic utility of artificial intelligence for left ventricular scar identification using cardiac magnetic resonance imaging-A systematic review.Cardiovascular digital health journal · 2021Article
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Authors and funding
8 authors.
Funding
Abstract
Background: Visualizing fibrosis on cardiac magnetic resonance (CMR) imaging with contrast enhancement (late gadolinium enhancement; LGE) is paramount in characterizing disease progression and identifying arrhythmia substrates. Segmentation and fibrosis quantification from LGE-CMR is intensive, manual, and prone to interobserver variability. There is an unmet need for automated LGE-CMR image segmentation that ensures anatomical accuracy and seamless extraction of clinical features. Objective: This study aimed to develop a novel deep learning solution for analysis of contrast-enhanced CMR images that produces anatomically accurate myocardium and scar/fibrosis segmentations and uses these to calculate features of clinical interest. Methods: Data sources were 155 2-dimensional LGE-CMR patient scans (1124 slices) and 246 synthetic "LGE-like" scans (1360 slices) obtained from cine CMR using a novel style-transfer algorithm. We trained and tested a 3-stage neural network that identified the left ventricle (LV) region of interest (ROI), segmented ROI into viable myocardium and regions of enhancement, and postprocessed the segmentation results to enforce conforming to anatomical constraints. The segmentations were used to directly compute clinical features, such as LV volume and scar burden. Results: Predicted LV and scar segmentations achieved 96% and 75% balanced accuracy, respectively, and 0 Conclusion: We developed and validated a deep neural network for automatic, anatomically accurate expert-level LGE- CMR myocardium and scar/fibrosis segmentation, allowing direct calculation of clinical measures. Given the training set heterogeneity, our approach could be extended to multiple imaging modalities and patient pathologies.
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