ReviewFrontiers in cardiovascular medicine2020
Deep Learning for Cardiac Image Segmentation: A Review.
Review in Frontiers in cardiovascular medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 275 papers, 4 of them syntheses that pooled it.
What it found
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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
275 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Deep learning for cardiac CT segmentation for congenital heart disease: A systematic review.PloS one · 2026Pooled it
- Machine learning-based myocardial infarction bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Medical image analysis on left atrial LGE MRI for atrial fibrillation studies: A review.Medical image analysis · 2022Pooled it
- Quality of reporting in AI cardiac MRI segmentation studies - A systematic review and recommendations for future studies.Frontiers in cardiovascular medicine · 2022Pooled it
- Multimodal Characterization of Atrial Fibrillation: From Patient-Specific Anatomy and Electrophysiology to Standardized Atrial Mapping.Tomography (Ann Arbor, Mich.) · 2026Review
- Article
- AI-based pulmonary artery to ascending aorta ratio on non-contrast CT for pulmonary hypertension: diameter vs. volume assessment.European heart journal. Imaging methods and practice · 2026Article
- Variance Extrapolated Class-Imbalance-Aware Domain Adaptive Myocardial Segmentation in Multi-Sequence Cardiac MRI.IEEE journal of biomedical and health informatics · 2026Article
- Deformable multi-head cross attention-based 3D U-net for context-aware multi-modal cardiac image segmentation with uncertainty-aware maps.Scientific reports · 2026Article
- Artificial Intelligence Across the Echocardiographic Workflow: A Narrative Review for Clinicians.Cureus · 2026Review
- Leveraging Self-Supervised Learning for Non-Invasive Intra-Cardiac Magnetic Resonance Oximetry Assessment.medRxiv : the preprint server for health sciences · 2026Article
- FA-SedLoRA: scene-driven fine-tuning and style disentanglement for domain-generalized cardiac MRI segmentation.Medical & biological engineering & computing · 2026Article
- A comparison of vendor artificial intelligence solutions for automated post-processing of short-axis cine images in cardiovascular magnetic resonance imaging.Scientific reports · 2026Article
- Airway segmentation on CT - A systematic review of machine learning tools.European journal of radiology open · 2026Article
- Point tracking as a temporal Cue for robust myocardial segmentation in echocardiography videos.International journal of computer assisted radiology and surgery · 2026Article
- Optimising Generalisable Deep Learning Models for CT Coronary Segmentation: A Multifactorial Evaluation.Journal of imaging informatics in medicine · 2026Article
- Fully automatic left ventricle segmentation in [Formula: see text]Rb PET/CT Using a semi-supervised nnU-net.EJNMMI research · 2026Article
- Three-dimensional visualization of arrhythmogenic substrate in mouse hearts using panoramic optical mapping and micro-computed tomography.Nature cardiovascular research · 2026Article
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- Artificial intelligence, extended reality and computational modelling in cross-sectional cardiovascular imaging in congenital heart disease: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
215 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound and major anatomical structures of interest (ventricles, atria, and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research.
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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.