dicom-series-to-volume
About dicom-series-to-volume
A script-based skill that converts a single CT DICOM series into a HU-scaled NIfTI volume and JSON summary. It is intended for data-processing workflows that need slice ordering and affine evidence, not clinical use.
Key features
- 01Convert a single CT series to NIfTI
- 02Sort slices by image position
- 03Apply rescale slope and intercept
- 04Derive affine from orientation and spacing
- 05Write volume metadata to JSON
Use cases
- 01Preparing a single-series CT volume for downstream processing
- 02Checking output shape, spacing, and orientation codes
- 03Recording affine and HU-range conversion evidence
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ promptingcompany/ nv-skills --skill dicom-series-to-volumeAfter installing, type/dicom-series-to-volumein Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
BENCHMARK.mdskill_manifest.yamlskill-card.mdskill.oms.sigevals/evals.jsonfixtures/generate_fixtures.pyscripts/series_to_volume.pyvalidators/output_schema.jsonDetails
From the project READMEdicom_series_to_volume
Purpose
- Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Not for multi-frame DICOM or clinical use.
- Use the wrapper exactly as documented; do not replace the upstream entrypoint with a handwritten implementation.
- Manifest I/O: inputs are
dicom_dir; outputs arenifti_volumeandresult_json.
Instructions
- Read
skill_manifest.yamlbefore changing arguments, side effects, or validation gates. - Run
scripts/series_to_volume.pythrough the documented command below; keep outputs under a caller-provided run directory. - If a host agent exposes
run_script, userun_script("scripts/series_to_volume.py", args=[...]); otherwise run the Bash/Python command shown below. - Check the emitted JSON and the paired
dicom_volume_quality_v1verifier before treating the run as evidence.
Available Scripts
| Script | Purpose | Arguments |
|---|---|---|
scripts/series_to_volume.py | Primary entrypoint declared by skill_manifest.yaml. | PATH_TO_DICOM_DIR [--output OUT.nii.gz] |
Prerequisites
- Runtime requirements: Python packages listed in
runtime.side_effects.pip_packages. - Run commands from the repository root unless an existing section below says otherwise.
Limitations
- Single-series only; multi-series input is rejected at preflight.
- Multi-frame DICOM (NumberOfFrames > 1 per file) not supported.
- Compressed transfer syntaxes (JPEG / JPEG2000 / RLE) not supported.
- No voxel reorientation. The affine is derived from DICOM headers and represented in NIfTI/RAS coordinates; a downstream gate (e.g. expected_axcodes) is expected to assert orientation before this volume is fed to a segmentation model.
- Not for clinical deployment, autonomous diagnosis, regulatory submission, production inference (use a vetted converter such as dcm2niix for that).
Troubleshooting
| Error | Cause | Fix |
|---|---|---|
| Missing dependency or import error | Runtime package drift from skill_manifest.yaml. | Install the packages declared in the manifest or use the documented setup command. |
| Empty or schema-invalid output | Wrong input path, unsupported modality, or upstream failure. | Re-run with a known fixture and inspect the wrapper JSON plus stderr. |
| Validation gate failure | Output violated a declared engineering invariant. | Keep the failed evidence pack and use the gate message to repair inputs or wrapper code. |
Reads one DICOM series, sorts slices by ImagePositionPatient, applies
RescaleSlope and RescaleIntercept, builds an affine from orientation and
spacing tags, and writes a .nii.gz plus JSON summary.
python scripts/series_to_volume.py PATH_TO_DICOM_DIR --output PATH_TO_OUT.nii.gz
For a trusted run with the paired verifier:
python -m eval_engine.run_trusted skills/dicom-series-to-volume \
--fixture PATH_TO_DICOM_DIR \
--out runs/dicom_series_to_volume_trusted
Key output fields: n_slices, series_instance_uid, output.path,
output.shape, output.spacing, output.axcodes, output.affine,
hu_range, and runtime.conversion_seconds.
Scope limits: single-series CT only; no multi-frame DICOM, compressed transfer syntax handling, RT structure sets, auto-reorientation, or clinical use.