FastSurfer Phase 4b: hypothalamus 27-class sub-seg (axial + coronal +
sagittal HypVINN networks, dual-input InputDenseBlock with m1 / m2
branches + softmax mod_weights). T1-only mode: T1
thick-slice duplicates as both modality channels,
weight_factor=[1, 0] zeros the T2 branch. All compute in
C/WGSL/SIMD128. Verified bit-identical (100.0000%) against PyTorch
HypVINN on brainix in T1-only mode.
✓ Ready. Brainix T1 in RAS-canonical form ships as demo input
(256³ u8, 16.8 MB). GPU accumulator = 256³ × 27 × 4 = 1.77 GB (single
buffer, fits under the 2 GB WebGPU cap).
⚠ T1-only mode. HypVINN was trained on paired T1+T2. This demo
runs the --mode t1 code path (a documented FastSurfer CLI
option), but the resulting segmentation is less accurate than with a
real registered T2 scan.
idle
Demo T1: brainix 0.9375 mm RAS 256³ (bit-exact parity with PyTorch).
Upload path: custom_ops_conform_u8 produces RAS 0.9375 mm 256³ u8
to match the fixture's scale (Zoom2d is hard-wired to this scale; dynamic Zoom2d deferred).