EF-Gated 3D Capsule Networks with Spatially Decoupled Routing for Clinical Phenotyping of Heart Failure Subtypes in Cardiac MRI.

Addressing the challenge of geometric feature modeling for heart failure subtype classification in cardiac magnetic resonance imaging (MRI), this study proposes an Ejection Fraction (EF) Gated 3D Capsule Network (EG3D-CapsNet). Traditional 3D convolutional neural networks (e.g., Res3DNet-50) achieve only 46.00% (±9.75%) classification accuracy on the Automated Cardiac Diagnosis Challenge (ACDC) dataset due to parameter redundancy and inability to integrate clinical indicators. The method of this study breaks through this performance bottleneck via three core innovations: (1) a spatially decoupled dynamic routing mechanism that independently processes feature interactions at each anatomical location, enhancing local geometric modeling capability; (2) an EF-gated attention module that achieves fine-grained alignment of imaging features and clinical indicators through a learnable biomarker scaling strategy; (3) an orthogonal initialization scheme for capsule transformation matrices, improving routing stability in high-dimensional feature spaces. Experiments on 150 five-class cardiac MRI cases show that EG3D-CapsNet achieves an average accuracy of 65.33% (±6.53%) with a 91% reduction in parameters, representing an absolute improvement of 19.33 percentage points over the baseline model. Ablation studies confirm the EF gating mechanism contributes an 18.04% accuracy gain, and visualization analysis reveals high correlation between capsule activation regions and myocardial pathological features. This method provides a new paradigm for cardiac imaging-assisted diagnosis that is high-precision, lightweight, and interpretable.