Abstract
Late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) imaging plays a crucial role in assessing myocardial scar tissues, aiding in the diagnosis and prognosis of cardiovascular diseases. However, accurately classifying scar tissue severity into mild and severe remains a challenge due to low contrast, noise interference, and inter-patient variability in LGE-CMR images. Existing methods often rely on manual assessment or traditional deep learning models that struggle with precise myocardium localization and discriminative feature extraction from scarred regions. To overcome these challenges, we propose a novel framework incorporating ScarYOLO, an optimized YOLOv8-based myocardium detection model, followed by contrastive myocardial scar learning (CMSL) for severity classification. ScarYOLO enhances myocardium localization accuracy, ensuring precise detection of scarred tissue. The detected myocardium is then processed using CMSL, which employs a fine-tuned Xception-based encoder trained on a labeled LGE-CMR dataset. CMSL leverages contrastive self-supervised learning to enhance feature representation and improve class separability between mild and severe scar regions. Additional dense layers and a classification head are appended to the encoder for final severity prediction. The proposed approach enhances myocardial scar detection accuracy while improving robustness in low-contrast LGE-CMR images. By leveraging ScarYOLO for precise segmentation and CMSL for effective classification, our model outperforms conventional deep learning methods in classifying scar tissue severity. Experimental evaluations demonstrate significant improvements in detection precision, classification accuracy, and model generalization, making it a reliable tool for automated myocardial scar assessment in clinical settings.