Dicom Series To Volume

dicomseriesto_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.
• 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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: NVIDIA-Medtech
• Source: NVIDIA-Medtech/medical-AI-skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.