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dicom-metadata-extract

NVIDIA/skills NVIDIA/skills

从 DICOM 文件中提取选定的元数据,并标记标准标签中是否包含受保护健康信息(PHI)。本工具不适用于匿名化处理或临床用途。

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更新时间 2026-09-25

DICOM 元数据提取

用途

  • 用于从单个 DICOM 文件中提取选定的元数据,并标记标准标签中是否包含受保护健康信息(PHI)。本工具不适用于匿名化处理或临床用途。
  • 请严格按照文档说明使用该封装类;请勿用自定义实现替换上游入口点。
  • 清单 I/O:输入为 dicom_path;输出为 metadata_json.

使用说明

  • 请先阅读 skill_manifest.yaml 。
  • 请 scripts/extract_metadata.py 执行下文所述的命令;将输出保存在调用方提供的运行目录下。
  • 如果主机代理暴露了 run_script,请使用 run_script("scripts/extract_metadata.py", args=[...]);否则运行下文所示的 Bash/Python 命令。
  • 检查生成的 JSON 并运行 medagent.verifiers.dicom_metadata_quality_v1 对证据包进行处理,之后才将该运行结果视为已审核的证据。

可用脚本

脚本 用途 参数
scripts/extract_metadata.py 由 skill_manifest.yaml 声明的主入口点。 PATH_TO_DICOM [--output OUT.json]

先决条件

  • 运行时要求: runtime.side_effects.pip_packages.
  • 除非下文现有章节另有说明,否则请从仓库根目录运行命令。

限制

  • 仅支持受 PS3.15 启发的小型标准标签子集;并非完整的“基本应用程序保密性配置文件”实现。
  • 未检查私有标签
  • 未检测到烧屏像素 PHI
  • 多帧处理功能有限
  • 不适用于临床部署、法规要求的去识别化、自主诊断以及面向患者的使用。

故障排除

错误 原因 解决方法
缺少依赖项或导入错误 运行时包与 skill_manifest.yaml. 安装清单中声明的包,或使用文档中记载的设置命令。
输出为空或模式无效 输入路径错误、模态不支持或上游处理失败。 请使用已知测试用例重新运行,并检查包装器 JSON 以及标准错误输出。
验证门失败 输出违反了声明的工程不变量。 保留失败的证据包,并利用验证闸门报错信息修复输入数据或封装器代码。

使用 pydicom 读取一个 DICOM 文件,并将 JSON 输出到标准输出。

python scripts/extract_metadata.py PATH_TO_DICOM
python scripts/extract_metadata.py PATH_TO_DICOM --output result.json

输出包括 transfer_syntax, modality按研究/序列/图像 分组的元数据, phi_present,以及 phi_tags_found.

请将此用作 Medical AI Skills 技能的最简端到端示例。请勿将其 用于匿名化、私有标签审查、像素级 PHI 检测或临床 解读。

对于二次证据审查,请生成一个可信运行:

python -m eval_engine.run_trusted skills/dicom-metadata-extract \
  --fixture skills/dicom-metadata-extract/fixtures/sample_ct.dcm \
  --out runs/dicom_metadata_trusted
在 GitHub 上查看
---
name: dicom-metadata-extract
description: Extracts selected metadata from a DICOM file and flags standard-tag PHI presence. Not for anonymization or clinical use.
license: Apache-2.0
---

# DICOM Metadata Extract

## Purpose
- Used for extracting selected metadata from one DICOM file and flagging standard-tag PHI presence. Not for anonymization 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_path`; outputs are `metadata_json`.

## Instructions
- Read `skill_manifest.yaml` before changing arguments, side effects, or validation gates.
- Run `scripts/extract_metadata.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/extract_metadata.py", args=[...])`; otherwise run the Bash/Python command shown below.
- Check the emitted JSON and run `medagent.verifiers.dicom_metadata_quality_v1` on evidence packs before treating the run as reviewed evidence.

## Available Scripts
| Script | Purpose | Arguments |
|---|---|---|
| `scripts/extract_metadata.py` | Primary entrypoint declared by skill_manifest.yaml. | `PATH_TO_DICOM [--output OUT.json]` |

## 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
- Small PS3.15-inspired standard-tag subset only; not a complete Basic Application Confidentiality Profile implementation.
- Private tags not checked
- Burnt-in pixel PHI not detected
- Multi-frame handling minimal
- Not for clinical deployment, regulatory de-identification, autonomous diagnosis, patient-facing use.

## 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 file with pydicom and emits JSON on stdout.

```bash
python scripts/extract_metadata.py PATH_TO_DICOM
python scripts/extract_metadata.py PATH_TO_DICOM --output result.json
```

Output includes `transfer_syntax`, `modality`, grouped study/series/image
metadata, `phi_present`, and `phi_tags_found`.

Use this as the smallest end-to-end example of a Medical AI Skills skill. Do not use
it for anonymization, private-tag review, pixel PHI detection, or clinical
interpretation.

For second-pass evidence review, generate a trusted run:

```bash
python -m eval_engine.run_trusted skills/dicom-metadata-extract \
  --fixture skills/dicom-metadata-extract/fixtures/sample_ct.dcm \
  --out runs/dicom_metadata_trusted
```

安装 dicom-metadata-extract

下载技能文件并将其解压到 .claude/skills/ 目录中。

下载ZIP

克隆仓库并复制技能文件到您的项目中。

git clone https://github.com/NVIDIA/skills/tree/main/skills/dicom-metadata-extract # Copy SKILL.md to your .claude/skills/ directory

复制 复制
快速设置: 将技能文件夹复制到 .claude/skills/ Claude 会自动检测并使用该技能
仓库 NVIDIA/skills

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