[Hendrik-code]

What

RandomSynthSegGPU takes zscore_output (default False). When set, each synthesised sample is standardised over its patch before it replaces the image:

"RandomSynthSegGPU": { "p": 1.0, "zscore_output": true, ... }

Why

With SynthSeg's default normalise=True the synthesis is min-max scaled to [0, 1], while the real patches beside it are z-scored. Measured on the 128³ training patches of four SpineGAN datasets (two cases each, five draws): the image std after SynthSeg is 0.16-0.20, against 0.86-0.91 for the Dixon in-phase and fat inputs.

Every later transform that works in absolute units then acts on the synthetic samples at several times its strength on the real ones. The visible case is RandomGaussianNoiseGPU, whose std is drawn from U(0, std): at the paper config's std: 1.0, the noise std exceeds the image's in about 80% of firings on a SynthSeg sample. In the measured sample, 16 of 20 noise firings after SynthSeg left the label boundaries indistinguishable from the background gradient (mean |∇I| on label boundaries ÷ mean |∇I| over the slice fell from 1.8-2.8 to 1.0-1.3). That affects the published SynthSeg (post) configuration (transform_params_paper-synthseg.json), where noise is on at p: 0.1 and SynthSeg at p: 1.0.

Behaviour

• Draws no random numbers: the same seed gives the same synthesis, up to the rescale (tested).
• Default False: shipped configs and their seeded outputs are unchanged.
• all_augmentations.json regenerated (smauglab matrix --write).

Tests

unit_tests/test_synthseg_zscore_output.py:

• the default still returns [0, 1];
• with the option set, every sample has mean 0 and std 1;
• the output is exactly the standardised default synthesis;
• the registry accepts the key.

Full suite: 477 passed, 13 skipped. mypy smauglab/ clean, pre-commit passes.

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