Tips and FAQs
Leveraging precomputed results
Whether to allow for manual intervention for tough cases, or simply to reduce processing time, NiBabies can allow the use of certain pre-computed files during processing. Initial support is limited to the following files:
Anatomical mask in T1w space
Antomical segmentation (aseg) in T1w space
To use pre-computed results, one or more BIDS Derivatives directories must be passed in to NiBabies using the
Derivative directories must include a
dataset_description.json and the required fields.
Additionally, files must include the
space-orig key-value pair in the name.
A sample layout of a derivatives directory can be found below:
my_precomputed/ ├── dataset_description.json └── sub-01 └── anat ├── sub-01_space-orig_desc-aseg_dseg.nii.gz ├── sub-01_space-orig_desc-brain_mask.json └── sub-01_space-orig_desc-brain_mask.nii.gz
Multi-atlas segmentation with joint label fusion
By default, NiBabies will run FSL FAST for tissue segmentation, and Infant FreeSurfer for segmentation labels.
However, you can instead use ANTs Joint Label Fusion to generate both, granted you provide multiple atlases with anatomicals / segmentations via the
When using this approach, there are a few assumptions being made:
The anatomicals are brain masked.
The labeled segmentations follow the FreeSurfer lookup table.
Here is an example layout of what the
--segmentation-atlases-dir flag expects:
$ tree JLF-templates JLF-templates/ ├── Template01 │ ├── Segmentation.nii.gz │ ├── T1w_brain.nii.gz │ └── T2w_brain.nii.gz └── Template02 ├── Segmentation.nii.gz ├── T1w_brain.nii.gz └── T2w_brain.nii.gz
More context on releases
Like other NiPreps, NiBabies follows Calendar Versioning (CalVer), in format of
In short, here is a quick heuristic on how new releases should be looked at:
MINORhas changed, it is a feature release, with substantial changes to the workflow.
YY.MINORmatch the version you used, but the
MICROhas changed, it is a bug-fix release. Check the release notes - if the fixes do not pertain to your data, there is no need to upgrade.
For more in-depth information, refer to the NiPreps release documentation.