Voxel-wise deep learning segmentation of hydroxyapatite and iodine in spectral photon-counting CT: A quantitative phantom study
Fig 5
Qualitative overlays of voxel-level segmentations on the external test scan.
Columns (left to right): 3D UNet, UNETR, R2UNet3D, Swin UNETR, ResUNet++, and SPFF–UNet (proposed). Predicted labels are argmax maps color-coded on grayscale SPCCT slices; all panels use identical windowing and a shared class colormap/legend. HA: hydroxyapatite; I: iodine. E: Energy bin (7-12, 12-15, 15-18, 18-21, 21-120 keV).