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dicom-series-to-volume

by @nvidiav
4.3(120)

この Skill は、単一シリーズのCT DICOMフォルダーをHU単位のNIfTIボリュームに変換し、RescaleSlope/Interceptとアフィン変換情報を自動適用します。医用画像の前処理やセグメンテーションモデルへの入力構築に適しており、手動変換の手間とエラーを大幅に削減します。

dicomniftictmedical-imagingconversionGitHub
インストール方法
npx skills add https://github.com/nvidia/skills --skill dicom-series-to-volume
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Before / After 効果比較

1 组
使用前

このSkillがない場合、開発者は手動でdcm2niixなどのツールを実行し、Rescaleパラメータとアフィン行列を処理するスクリプトを作成する必要があり、通常30分以上かかり、パラメータ誤りによるボリューム変形が発生しやすかった。

使用後

このSkillはワンステップで変換を実行し、DICOMヘッダーを自動解析します。1回の変換は約2分で完了し、標準的なNIfTIファイルと検証JSONを出力し、品質は信頼できます。

SKILL.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

ScriptPurposeArguments
scripts/series_to_volume.pyPrimary 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

ErrorCauseFix
Missing dependency or import errorRuntime package drift from skill_manifest.yaml.Install the packages declared in the manifest or use the documented setup command.
Empty or schema-invalid outputWrong input path, unsupported modality, or upstream failure.Re-run with a known fixture and inspect the wrapper JSON plus stderr.
Validation gate failureOutput 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.

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統計データ

インストール数1.9K
評価4.3 / 5.0
バージョン
更新日2026年9月30日
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作成2026年7月26日
最終更新2026年9月30日
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