MECardNet: A novel multi-scale convolutional ensemble model with adaptive deep supervision for precise cardiac MRI segmentation

Abstract

Accurate segmentation of the left ventricle, right ventricle, and myocardium is essential for estimating key cardiac parameters in diagnostic procedures. However, automating Cardiovascular Magnetic Resonance Imaging (CMRI) segmentation faces challenges from diverse imaging vendors and protocols. This study introduces MECardNet framework as an innovative multiclass CMRI segmentation model, representing a prominent advancement in the field. MECardNet leverages a Multiscale Convolutional Mixture of Experts (MCME) ensemble technique with Adaptive Deep Supervision, seamlessly integrated into the U-Net architecture. The MCME framework improves representation learning in the U-Net workflow. It does this by adaptively adjusting the contribution of U-Net layers in the ensemble for better data modeling. Additionally, MECardNet incorporates a cross-additive attention mechanism, an EfficientNetV2L backbone, and a specialized compound loss function, leading to enhanced model performance. Through 10-fold Cross-Validation (CV) analysis on the ACDC dataset, MECardNet surpasses baseline models and state-of-the-art methods, showcasing promising performance levels with evaluation metrics such as Dice Similarity Coefficient (DSC) of 96.1 ± 0.4 %, Jaccard coefficient of 92.2 ± 0.4 %, Hausdorff distance of 1.7 ± 0.1 and mean absolute distance of 1.6 ± 0.1. Further validation on the M&Ms-2 dataset and a local dataset confirms promising performance of MECardNet, with DSC of 94.3 ± 0.7 % and 94.5 ± 0.6 %, respectively. The proposed MECardNet framework establishes a new benchmark in CMRI segmentation by outperforming existing models, offering efficient and reliable computer-aided technologies for cardiovascular disease diagnosis, with the potential for significant impact in the field. Researchers can access MECardNet repository and results on GitHub1 for comprehensive exploration and utilization. © 2024 Elsevier Ltd