Quick Start¶
This page covers the three most common commands to get you up and running.
1. Convert DICOMs to BIDS¶
oncoprep-convert /path/to/dicoms /path/to/bids --subject 001
This converts raw DICOM directories into a
BIDS-valid dataset using dcm2niix.
2. Run preprocessing¶
oncoprep /path/to/bids /path/to/derivatives participant \
--participant-label sub-001
This runs the full anatomical preprocessing pipeline:
Validates the BIDS dataset
Collects T1w, T1ce, T2w, and FLAIR images
Conforms images to 1 mm isotropic resolution
Co-registers all modalities to the T1w reference
Skull-strips the brain
Normalizes to MNI152NLin2009cAsym template space
Writes BIDS derivative outputs
When segmentation is enabled (--run-segmentation), template registration
is deferred until after the tumor mask is available, so it can be used
as a cost-function exclusion mask for ANTs SyN. The pipeline becomes:
Anatomical preprocessing (conform, co-register, skull-strip, N4)
Tumor segmentation (native space)
Radiomics extraction (native-space mask, optional)
Template registration with dilated tumor mask as exclusion region
VASARI feature extraction (template-space mask, optional)
Writes BIDS derivative outputs
3. Run with quality control¶
Note: MRIQC integration is temporarily disabled in this release. The
--run-qcflag is accepted but ignored. It will be re-enabled in a future version.
# TEMPORARILY DISABLED — flag is accepted but has no effect
oncoprep /path/to/bids /path/to/derivatives participant \
--participant-label sub-001 \
--run-qc
4. Run with segmentation¶
# Default: nnInteractive zero-shot model (no Docker needed)
oncoprep /path/to/bids /path/to/derivatives participant \
--participant-label sub-001 \
--run-segmentation --default-seg
# Full Docker ensemble (GPU required)
oncoprep /path/to/bids /path/to/derivatives participant \
--participant-label sub-001 \
--run-segmentation
Output structure¶
After processing, derivatives follow BIDS conventions:
derivatives/
└── oncoprep/
└── sub-001/
└── anat/
├── sub-001_desc-preproc_T1w.nii.gz
├── sub-001_desc-preproc_T1w.json
├── sub-001_desc-brain_mask.nii.gz
├── sub-001_space-MNI152NLin2009cAsym_desc-preproc_T1w.nii.gz
└── sub-001_desc-tumor_dseg.nii.gz # if segmentation was run
Next steps¶
Read the full Tutorial: End-to-End Neuro-Oncology Preprocessing for a worked example
See Command-Line Interface for all command-line options
Check the Workflows reference for Python API usage