Integrating physics guidance into deep learning for cine cardiovascular segmentation

Cine cardiovascular magnetic resonance (CMR) provides dynamic sequences for quantifying clinically important indices such as ventricular volumes and ejection fraction. A fast and accurate deep segmentation model is therefore valuable for timely analysis, reducing manual workload and improving reproducibility in routine workflows. However, most existing segmentation models remain largely data-driven and lack explicit physical constraints, which can reduce prediction plausibility under realistic acquisition variability. In this work, we present PGE-UNet, an efficient segmentation framework for cine CMR that incorporates radiofrequency-related information to promote physically plausible outputs. Specifically, it employs the proposed Noise-Aware Encoder with self-supervised noise estimation and smoothing mechanism for robust feature encoding, and the Physics-Regularized Decoder that enforces Maxwell-Helmholtz consistency under a pseudo transmit-field prior. Since transmit-field measurements are typically unavailable in standard protocols, we further introduce a simulator-based procedure to synthesize the pseudo transmit-field prior, enabling physics-regularized training without additional mapping sequences. Experiments on three public cine CMR benchmarks demonstrated strong accuracy in both DICE and HD95, achieving foreground average DICE of 0.9152 on ACDC, 0.8605 on M&Ms, and 0.8548±0.0762 on SCD, while remaining lightweight with only 1.60 million parameters in the default setting. Under CPU-only inference, the default configuration recorded an average latency of 72.38 ms per case, making it suitable for deployment in time-sensitive and resource-constrained environments. Our source code is publicly available at https://github.com/QuocKhanhLuong/PGE-UNet.