An Efficient Lightweight Transformer Framework for Precise Cardiac Segmentation via Frequency-Domain Decomposition

Cardiac MRI segmentation remains hindered by two persistent obstacles. First, boundary ambiguity arises from poor contrast between the myocardium and blood pool, the presence of papillary muscles and trabeculations within the cavity, partial-volume artifacts, and locally thin myocardial walls, all of which lead to contour discontinuities and erosion. Second, computational burden becomes prohibitive as conventional self-attention scales quadratically with the spatial and slice dimensions of high-resolution CMR volumes.To address these challenges, we introduce an efficient lightweight Transformer specifically designed for cardiac MRI. Our framework comprises two key modules: (1) a Laplacian pyramid decomposition, which separates features into high-, mid-, and low-frequency sub-bands, where the proposed lightweight attention compresses key–value representations to reduce complexity while retaining high-frequency boundary cues; and (2) a multi-scale efficient fusion bridge integrated into skip connections, which aligns cross-scale semantics and enhances residual details with minimal overhead. Experiments on ACDC and MS-CMRSeg 2019 show 0.5% relative improvements in Dice and 5.4% reductions in Hausdorff Distance (HD) over state-of-the-art models, achieving superior boundary accuracy and reduced computational complexity on high-resolution, multi-slice cardiac MRI.