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A Survey on Cardiac MRI Segmentation: From Classical Methods to State-of-the-art Deep Learning

Authors :

Hamed Aghapanah Roudsari¹ Reza Saboori Amleshi² Ali Saeeidi Rad³ Masoud Noroozi⁴

1- دانشگاه علوم پزشکی اصفهان 2- بیمارستان شهید رجایی تهران 3- گروه مهندسی پزشکی، دانشکده مهندسی، دانشگاه اصفهان 4- گروه مهندسی پزشکی، دانشکده مهندسی، دانشگاه اصفهان

Keywords :

Cardiac MRI Segmentation،Deep Learning،Hybrid Methods،Machine Learning Methods،Survey

Abstract :

Accurate and timely diagnosis of cardiac pathologies relies heavily on Cardiovascular Magnetic Resonance (CMR) imaging, the gold standard for assessing myocardial structure, function, and tissue characteristics. A critical step in CMR analysis is the segmentation of cardiac chambers—particularly the left ventricle, right ventricle, and myocardium—to derive essential clinical parameters such as ejection fraction, ventricular volumes, and myocardial mass. Manual segmentation, while accurate, is labor-intensive and subject to inter-observer variability, limiting its scalability in clinical practice. This has driven the need for automated, reliable, and reproducible segmentation methods. Classical approaches, including active contours and level sets, struggle with noise and low contrast. In contrast, deep learning models—especially U-Net variants, transformers, and hybrid architectures—have achieved expert-level accuracy, enabling fully automated quantification. However, challenges remain in generalizability across scanners and centers, robustness to artifacts, model interpretability, and integration into clinical workflows. This review addresses these gaps by systematically evaluating state-of-the-art methods, highlighting advances in deep and hybrid models, public benchmarks, and emerging solutions such as explainable AI and federated learning. The study underscores the necessity of this work to bridge the gap between research innovation and real-world clinical deployment, ensuring safe, trustworthy, and scalable AI-powered CMR analysis.

Papers List

List of archived papers

TransFuse++: A Hybrid CNN-Transformer Architecture with Cross-Attention, Temporal Modeling, and Uncertainty Estimation for Medical Image Segmentation

Masoud Noroozi - Sayna Jamaati - Hamed Aghapanah - Ali Saeeidi Rad - Mahsa Asadi Anar - Ali Darzi - Mahla Shokouhfar - Helia Sadat Kazemi - Mohammadreza Ghahari - Mohammad Saeed Soleimani Meigoli - Jafar Majidpour - Hossein Arabi - Ali Reza Karimian