NIfTI Dataset Management

NIfTI dataset toolkit for CT/MRI deep-learning pipelines: slice extraction, registration, QC, preprocessing — no ML dependencies.

Official GitHub repository available here.

🚀 Quick start

pip install nidataset            # add [yaml] for YAML pipeline / QC configs

import nidataset as nid

print(nid.qc.check_dataset("scans/").status)  # orientation, spacing, NaN, ... before anything else
nid.resampling_dataset("scans/", "resampled/", desired_volume=(224, 224, 128), saving_mode="folder")
nid.extract_slices_dataset("resampled/", "slices/", view="axial", saving_mode="view")
nid.extract_annotations_dataset("labels/", "annotations/", view="axial", saving_mode="view")

Or describe the whole chain in one config and run it from the shell — see Pipelines:

nii run pipeline.yaml            # QC -> slices -> registration -> ... + run_manifest.json

🧭 Where to go next

I want to…	Go to
Install, extras, FSL, templates	Installation
Run a reproducible multi-step pipeline	Pipelines (`nii run`)
Check a dataset before training	Quality Control
Rotate / flip / crop / convert from the shell	Quick Operations (`nii`)
Look up a function	Package Functions
See what changed	Changelog
Cite nidataset	`CITATION.cff`

Check here for a complete NIfTI guide

👨‍💻 Authors

⚖️ License

MIT License