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<p>The Cardiac Magnetic Resource Imaging (CMRI) segmentation is considered vital for computing the cardiac function, and clinical decision-making. Conventional methods such as deformable models and atlas-based techniques are getting limited by the manual feature engineering. The beginning of deep learning (DL), attention mechanisms, and hybrid architectures is transforming the cardiac image analysis. However, challenges tend to remain in allowing the precise boundary delineation across the multi-center datasets, functional indices, and the mitigation of demographic biases. In addition, the selection of optimal loss functions, and the ensemble methods are significantly impacting the segmentation performance. This review is examining literature from 2000 to 2025, which is covering the classical, convolutional, U-Net and 3D U-Net variants, attention-based networks, Transformer models, hybrid CNN–Transformer frameworks, and the self-supervised learning methods. We are analyzing the training methods, such as loss functions (Dice, cross-entropy, Focal, Lovász-softmax, hybrid), data augmentation, preprocessing methods (CLAHE, intensity normalization, autoencoders), and the ensemble methods. Various performance metrics that includes Dice Similarity Coefficient, Intersection over Union, Hausdorff Distance, mean contour distance, and population-scale clinical parameters are then compared. The review is showing emerging trends, which includes bias detection, semi-supervised and self-supervised methods for clinical adoption. DL and hybrid frameworks are performing better than the classical methods that tends to achieve DSC &gt; 0.95, HD &lt; 2&#xa0;mm, and the ejection fraction estimation within ± 5% of manual reference. Also, the Attention mechanisms, multi-scale ensembles, and the hybrid CNN–Transformer pipelines are improving the boundary precision across the datasets that includes UK Biobank, ACDC, CAP, M&amp;Ms-2, and the HVSMR-2. Finally, Self-supervised methods are showing a strong performance in the low-labeled data regimes, while studies on demographic bias is emphasizing the need for an equitable AI model design.</p>

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Advancement in Cardiac MRI Segmentation Using Deep Learning Architectures, Training Strategies, and Clinical Applications

  • 期刊论文

  • 发表日期：

    2026年6月22日

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  • Rajani Dharanikota,
  • Sreedhar Kollem

摘要

The Cardiac Magnetic Resource Imaging (CMRI) segmentation is considered vital for computing the cardiac function, and clinical decision-making. Conventional methods such as deformable models and atlas-based techniques are getting limited by the manual feature engineering. The beginning of deep learning (DL), attention mechanisms, and hybrid architectures is transforming the cardiac image analysis. However, challenges tend to remain in allowing the precise boundary delineation across the multi-center datasets, functional indices, and the mitigation of demographic biases. In addition, the selection of optimal loss functions, and the ensemble methods are significantly impacting the segmentation performance. This review is examining literature from 2000 to 2025, which is covering the classical, convolutional, U-Net and 3D U-Net variants, attention-based networks, Transformer models, hybrid CNN–Transformer frameworks, and the self-supervised learning methods. We are analyzing the training methods, such as loss functions (Dice, cross-entropy, Focal, Lovász-softmax, hybrid), data augmentation, preprocessing methods (CLAHE, intensity normalization, autoencoders), and the ensemble methods. Various performance metrics that includes Dice Similarity Coefficient, Intersection over Union, Hausdorff Distance, mean contour distance, and population-scale clinical parameters are then compared. The review is showing emerging trends, which includes bias detection, semi-supervised and self-supervised methods for clinical adoption. DL and hybrid frameworks are performing better than the classical methods that tends to achieve DSC > 0.95, HD < 2 mm, and the ejection fraction estimation within ± 5% of manual reference. Also, the Attention mechanisms, multi-scale ensembles, and the hybrid CNN–Transformer pipelines are improving the boundary precision across the datasets that includes UK Biobank, ACDC, CAP, M&Ms-2, and the HVSMR-2. Finally, Self-supervised methods are showing a strong performance in the low-labeled data regimes, while studies on demographic bias is emphasizing the need for an equitable AI model design.