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dicom-series-to-volume

by @nvidiav
4.3(120)

该 Skill 将单序列 CT DICOM 文件夹转换为 HU 单位的 NIfTI 体积,自动应用窗宽窗位并提取仿射变换信息。适用于医学影像预处理、分割模型输入构建等场景,显著减少手动转换的繁琐与错误。

dicomniftictmedical-imagingconversionGitHub
安装方式
npx skills add https://github.com/nvidia/skills --skill dicom-series-to-volume
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Before / After 效果对比

1 组
使用前

在没有这个技能之前,开发者需要手动调用 dcm2niix 等工具,编写脚本处理 Rescale 参数和仿射矩阵,通常耗时 30 分钟以上,且容易因参数错误导致体积变形。

使用后

此 Skill 一键执行,自动处理 DICOM 头解析与转换,单次转换仅需 2 分钟左右,输出标准 NIfTI 文件及验证 JSON,质量可靠。

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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统计数据

安装量1.9K
评分4.3 / 5.0
版本
更新日期2026年9月30日
对比案例1 组

用户评分

4.3(120)
5
37%
4
43%
3
13%
2
5%
1
2%

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0.0

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时间线

创建2026年7月26日
最后更新2026年9月30日
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