Deep Learning Myocardial Infarction Segmentation Framework from Cardiac Magnetic Resonance Images

Segmentation of myocardial infarction (MI) is a crucial task in the field of heart disease theranostics. Cardiac magnetic resonance imaging (MRI) is a well-known non-invasive imaging technique that provides comprehensive insights into the structure and function of the heart. However, manually interpreting myocardial infarction from multiple MRI frames is time-consuming, labor-intensive, and prone to errors. This study aims to develop an end-to-end deep learning framework that can automatically segment myocardial infarction (MI) and persistent microvascular obstruction (MVO) among the normal tissues of left ventricle (LV), normal myocardium (Myo), and the remaining normal foreground (BG). The proposed framework includes various stages, such as cardiac MR image collection, preprocessing via three enhancement techniques, splitting and training set augmentation, selection of the most suitable artificial intelligence (AI) segmentation model, and performance evaluation and comparison. For the multi-class segmentation process, we adopt and develope four AI state-of-the-art models: UNet, U-Net_VGG16, SegNet, and ResUnet, which are well-regarded for their effectiveness in image segmentation across different computer vision domains. The publicly available benchmark EMIDEC MRI dataset is utilized for training and evaluating the proposed segmentation framework. The ResU-Net achieved the top performance compared to other AI models, recording an overall accuracy (Acc) of 88.48%, recall (Re) of 85.24%, precision (Pre) of 85.46%, F1-score of 85.35, and MIoU of 84.23%. Comparing with the original dataset (without preprocessing), the CLAHE preprocessing improves the ResU-Net segmentation performance by 2.19% and 3.08% in terms of average F1-score and MIoU for all classes (LV, Myo, MI, and MVO). Therefore, the proposed AI segmentation framework demonstrates its potential for effectively performing multi-class segmentation of cardiac diseases from MRI images.