MAGEFormer: Learning Metric-Consistent Representations for Anisotropic CT Segmentation
Vision Transformers (ViTs) have shown strong performance in volumetric segmentation, but their effectiveness on clinical CT is limited by an isotropic Euclidean lattice assumption. This conflicts with anisotropic CT acquisition, leading to two key issues: (1) a metric mismatch between voxel indices and physical anatomy, and (2) accuracy degradation from isotropic resampling. To address this, we propose MAGEFormer, a geometry-calibrated framework that embeds physical metric constraints directly into representation learning. Our method introduces Metric-Adaptive Spatial Embedding (MASE) to calibrate positional frequencies using voxel spacing, Geometry-Constrained Attention (GCA) to suppress...
| Authors | Jiaying Li, Paolo Remagnino |
|---|---|
| Primary category | cs.CV |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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