"Multi-Material SPCCT Phantom Dataset with Five Energy Bins: Hydroxyapatite (50\u2013800 mg\/cm\u00b3), Iodine (5\u201315 mg\/mL), and Soft-Tissue"

"Spectral photon-counting CT (SPCCT) enables material segmentation from multi-energy measurements, but many conventional 3D segmentation backbones downsample the energy-bin axis, weakening the spectral cues needed to separate hydroxyapatite (HA) from iodine and to resolve discrete concentrations. We propose S\\textsuperscript{3}UNet, a spectral--spatial 3D U-Net that preserves the spectral axis via anisotropic pooling\/upsampling and integrates spectral-aware attention, a Swin-inspired channel-mixing bottleneck, and a refinement head. On a SPCCT phantom dataset (five scans; five energy bins; HA50--HA800, iodine I5--I15, soft-tissue equivalents, and water), trained on four scans and evaluated on a held-out external scan, S\\textsuperscript{3}UNet achieves $0.84\\pm0.04$ foreground macro-averaged Dice and $0.86\\pm0.03$ foreground macro-averaged precision, outperforming 3D U-Net ($0.48\\pm0.07$; $0.49\\pm0.09$) and SwinUNETR ($0.65\\pm0.02$; $0.55\\pm0.01$). Single-factor ablations show the largest degradations in macro-averaged performance for IsoPool and NoRefine. Finally, segmentation-derived rod-wise readout achieves the lowest pooled calibration MAE (HA+I) without a dedicated regression head. These results support spectral-axis-preserving architectures for robust SPCCT material labeling and segmentation-driven quantification."