Deep Learning Approaches for Cardiac MRI Segmentation: A Narrative Review of Methods and Clinical Applications

Abstract

Cardiac magnetic resonance imaging (CMR) is the reference standard for noninvasive assessment of cardiac structure, function, and tissue characterization, but manual segmentation remains time-intensive and variable. We systematically reviewed deep learning methods for cardiac MRI segmentation published from 2015 to 2026 to evaluate architectural progress, real-world robustness, and barriers to clinical translation. Searches of Scopus, MEDLINE, Web of Science, and PubMed identified 1,769 records; 278 met eligibility criteria, and 51 studies were selected for detailed synthesis. Deep learning models, particularly U-Net derivatives and newer transformer- and state-space-based approaches, achieved strong benchmark performance, often with Dice coefficients above 0.90. However, performance frequently declined under domain shift, motion artifact, rare pathology, and multi-center deployment. Overall, benchmark success has outpaced clinical readiness. Future progress will depend less on architectural complexity and more on pathological diversity, quality control integration, external validation, and prospective outcome-based studies.