Resource-Aware Evolutionary Neural Architecture Search for Cardiac MRI Segmentation

Cardiac magnetic resonance (CMR) segmentation underpins quantitative assessment of ventricular structure and function, yet reliable delineation remains difficult due to low tissue contrast, fuzzy boundaries, and inter-scan variability. We present CardiacNAS, an evolutionary neural architecture search (NAS) framework that couples a U-Net-like supernet with a cardiac-aware search space spanning depth/width, kernel size, filter size, attention, fusion, activation, dropout, and residual scaling. The search is explicitly resource-aware, jointly optimizing dice similarity coefficient (DSC) and 95th-percentile Hausdorff distance (HD95) versus model size and floating-point operations (FLOPs) under fixed compute budgets. Candidate architectures are instantiated from the supernet, trained with proxy budgets, and evolved through crossover, mutation, and elitist selection. We evaluate on the ACDC dataset and compare against six state-of-the-art methods, using qualitative comparisons, learningcurve analyses, and design-factor correlation studies. The resulting model attains 93.22 % average DSC and $4.73, ~\text{mm}$ HD95 with $\mathbf{3. 5 8 M}$ parameters and $\mathbf{1 4. 5 6}$ GFLOPs, demonstrating a favorable accuracy-efficiency trade-off. Analyses indicate that searched attention and fusion choices, together with residual scaling, contribute to improved boundary fidelity and stability. CardiacNAS offers a principled, resource-aware approach to deployable CMR segmentation with transparent reporting of architectural complexity and compute budgets.