ArticleiScience2026
High frequency edge network for accurate cardiac structure segmentation.
Article in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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7 authors.
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Abstract
Accurate cardiac structure segmentation is intrinsically a boundary delineation problem, where discriminative anatomical cues are largely encoded in high frequency components. We develop High Frequency Edge Network (HF-EdgeNet), a high frequency driven encoder decoder framework that incorporates structural cues throughout cardiac magnetic resonance imaging (MRI) segmentation. Specifically, HF-EdgeNet uses the High Frequency Edge Transformer (HF-EdgeT) to inject high frequency guidance into self-attention, introduces the High Frequency Adaptive module (HF-Adapte) to compensate for high frequency degradation during down sampling, and designs the Semantic Edge Bridge (SEB) block for high frequency semantic re-alignment during decoding. Experiments on ACDC and M&Ms show improved average dice scores and favorable boundary related performance over strong segmentation baselines. These results support explicit high frequency modeling for boundary sensitive cardiac image segmentation.
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