feat(apps): OME-Zarr/DICOM registration, IMPACTReg PCA, and app tests + examples - #37
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… PCA loss Dataset readers auto-detect an OME-Zarr / DICOM store from its markers on read, and the IMPACTReg feature loss gains a PCA reduction (torch, behaviourally identical to itk-impact's pca_fit/pca_transform). Covered by unit tests.
…, and app CLI smoke tests The impact_reg registration app reuses model outputs and minimises orchestrator I/O; app.json inputs may declare default: ones|zeros so konfai-apps synthesises a missing optional input from Volume_0's header (no pixel load). CLI wrappers for IMPACT-Seg/Synth and MR/TotalSegmentator are smoke-tested, and the register/eval CLI syntax is fixed.
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OME-Zarr/DICOM store handling, the IMPACT-Reg registration app, and its examples.
What's in it
pca_fit/pca_transform).impact_regregistration app reuses model outputs and minimises orchestrator I/O;app.jsoninputs may declaredefault: ones|zerosso konfai-apps synthesises a missing optional input fromVolume_0's header (no pixel load). CLI wrappers for IMPACT-Seg/Synth and MR/TotalSegmentator are smoke-tested; the register/eval CLI syntax is fixed.NCC ≈ 0.84 → ≈ 0.99, seam-free) and Colab notebooks for the packaged model apps.Verification
Core + apps + konfai-mcp suites are green on the full stack; the store-format detection and the PCA metric are covered by unit tests.