Comments (2)
Hum, one possible cause is that there are two APIs depending on the slicewise parameter:
spinalcordtoolbox/spinalcordtoolbox/registration/core.py
Lines 361 to 384 in a7e538c
BUT: The default slicewise is 0. So it shouldn't matter...
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When tweaking params, eg; adding multi-step shrink factors (4, 2, 1), I do see a difference in the src/src_reg, so maybe the original issue is simply data-related (ie: no convergence, and hence registration matrix is identity).
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Related Issues (20)
- Output ANTs syntax in moco for easier debugging HOT 2
- Suspicious application of a mask in moco HOT 5
- Verbose CLI arg (`sct_apply_transfo -v`) is not passed to the subprocess call to `antsApplyTransforms` HOT 1
- Update SCIseg model to the latest version
- Improve performance of `sct_analyze_lesion -f` by avoiding redundant calls to `np.where` HOT 1
- `matplotlib==3.9.1` no longer installs wheels, instead tries to build on Windows (but fails in CI) HOT 2
- `sct_analyze_lesion` - pandas warnings are mixing with the function output HOT 3
- `sct_analyze_lesion` - sagittal slices are alwayes reported in RPI HOT 1
- Clarify steps for installing SCT onto ComputeCanada Graham (`requirements.txt` conflicts) HOT 10
- `sct_analyze_lesion` crashes if there is no lesion on the midsagittal slice
- `DEPRECATION: Legacy editable install` during `pip install -e .` step of `install_sct` HOT 2
- `sct_analyze_lesion` - add support for spinal levels HOT 1
- `sct_analyze_lesion` - output lesion length and width for the midsagittal slice HOT 1
- `sct_analyze_lesion` - tissue bridges QC - possible improvements HOT 2
- Tensorflow-based ONNX models using a lot of memory on large image HOT 3
- Standardize on matplotlib style for SCT (implicit vs. explicit, `pyplot`-managed figures)
- EPI spinal cord seg model is missing `nnUNetTrainer` folder, causing it to crash on v6.4
- `sct_deepseg` - allow to specify path/URL to custom model
- Output physical coordinates in the ".csv" file HOT 2
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