Robust ventricular segmentation in cardiac MRI via fused attention and capsule networks

Objectives An effective automatic system for ventricular segmentation from MRI is vital for diagnosing cardiovascular diseases, yet challenges persist due to anatomical variations and artifacts. Methods We preprocess cardiac MRI with min-max normalization, then propose a hybrid segmentation network (ResFAU-net) integrating residual blocks, attention gates, and a Fused Accumulation Bridge module to delineate ventricle boundaries. The segmented regions are classified by the HAMC3 model, which combines cascaded capsule networks, CNNs, and hierarchical attention, with parameters optimized via the Coati Optimization Algorithm (COA). Results Rigorous assessment on our CMRI dataset using metrics (Dice, IoU, accuracy, precision, etc.) demonstrates the model’s high performance in segmenting and classifying the left and right ventricles achieving an IoU of 96.29 % and accuracy of 99.03 %. Conclusions The proposed ResFAU-net and HAMC3 framework offers a robust, end-to-end solution for precise ventricular cardiac analysis, demonstrating strong potential to automate and enhance the efficiency of cardiovascular diagnosis in clinical MRI workflows.