dicom-series-to-volume
Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Not for multi-frame DICOM or clinical use.
git clone --depth 1 https://github.com/NVIDIA/skills /tmp/dicom-series-to-volume && cp -r /tmp/dicom-series-to-volume/skills/dicom-series-to-volume ~/.claude/skills/dicom-series-to-volumeSKILL.md
# dicom_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 are `nifti_volume` and `result_json`.
## Instructions
- Read `skill_manifest.yaml` before changing arguments, side effects, or validation gates.
- Run `scripts/series_to_volume.py` through the documented command below; keep outputs under a caller-provided run directory.
- If a host agent exposes `run_script`, use `run_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_v1` verifier 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`.
- NiBabel 5.4 or newer is required so extreme-oblique axes remain labeled consistently across reorientation.
- 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.
```bash
python scripts/series_to_volume.py PATH_TO_DICOM_DIR --output PATH_TO_OUT.nii.gz
```
For a trusted run with the paired verifier:
```bash
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.>-
Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
|
|
Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.
Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.
Calibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API. Use when user has local MP4s and says 'calibrate my videos', 'run AMC on these videos', or similar. For RTSP/live streams, use amc-run-rtsp-calibration instead.
Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.