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nvidia/skills · Official

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.

All-time #6717 First seen May 31, 2026
8-week activity · all time api

Installation

$ npx skills add nvidia/skills --skill dicom-series-to-volume
View on skills.sh ↗ GitHub repository ↗

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
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Repository health

Stars 3.2K
License LICENSE-APACHE
Default branch main
Open issues 5
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

LicenseApache-2.0
Allowed toolsBash
More metadata
author
NVIDIA MedTech Team
tags
["MedTech","DICOM","NIfTI"]

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,701 B
  • docs SUMMARY.md 162 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1,800 installs

SKILL.md

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 dicomdir; outputs are niftivolume and result_json.

Instructions

  • Read skill_manifest.yaml before changing arguments, side effects, or validation gates.
  • Run scripts/seriestovolume.py through the documented command below; keep outputs under a caller-provided run directory.
  • If a host agent exposes runscript, use runscript("scripts/seriestovolume.py", args=[...]); otherwise run the Bash/Python command shown below.
  • Check the emitted JSON and the paired dicomvolumequality_v1 verifier before treating the run as evidence.

Available Scripts

Script Purpose Arguments
scripts/seriestovolume.py Primary entrypoint declared by skill_manifest.yaml. PATHTODICOM_DIR [--output OUT.nii.gz]

Prerequisites

  • Runtime requirements: Python packages listed in runtime.sideeffects.pippackages.
  • 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.

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: nslices, seriesinstanceuid, output.path, output.shape, output.spacing, output.axcodes, output.affine, hurange, 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.