メニュー
Scan medical imaging directories and generate structured JSON or CSV manifests with file-level metadata for DICOM and NIfTI assets.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | dataset-manifest-builder |
| description | Scan medical imaging directories and generate structured JSON or CSV manifests with file-level metadata for DICOM and NIfTI assets. |
| version | 0.1.0 |
| author | Medical AI Team <team@example.com> |
| license | MIT |
| tags | ["dataset","manifest","dicom","nifti","metadata"] |
| maturity | experimental |
| category | dataset-management |
| requires_extras | ["core","dicom"] |
This skill scans a directory tree, discovers supported medical image files, extracts lightweight header metadata, and writes a machine-readable manifest for downstream QC, curation, or pipeline orchestration.
Use this skill when you need a reproducible inventory of imaging data before preprocessing or model training.
pip install .[dicom]
Dependencies:
python scripts/main.py --input-dir /path/to/images --output manifest.json [OPTIONS]
| Argument | Required | Description |
|---|---|---|
--input-dir | Yes | Directory containing medical image files |
--output | Yes | Output manifest path (.json or .csv) |
| Option | Default | Description |
|---|---|---|
--format | auto | Output format: json, csv, or infer from extension |
--recursive | false | Recursively scan subdirectories |
--pattern | * | Additional glob filter applied before header inspection |
--group-by | none | Group rows by patient, study, or series |
--verbose | false | Enable verbose logging |
--version | - | Show version and exit |
--help | - | Show help and exit |
Input is a filesystem directory containing a mix of supported and unsupported files.
Supported discovery targets:
.nii, .nii.gz){
"metadata": {
"generated_at": "2024-01-15T10:30:00Z",
"source_directory": "/path/to/images",
"recursive": true,
"total_files": 150
},
"summary": {
"file_count": 150,
"modalities": ["CT", "MR"],
"patients": 25,
"studies": 40,
"series": 85
},
"entries": [
{
"file_path": "patient001/study001/series001/image001.dcm",
"absolute_path": "/abs/path/to/image001.dcm",
"modality": "CT",
"patient_id": "PAT001",
"study_id": "STD001",
"series_id": "1",
"file_type": "DICOM",
"file_size_bytes": 524288
}
]
}
| Code | Meaning |
|---|---|
| 0 | Success |
| 1 | Validation error |
| 2 | Runtime error |
| 3 | Usage error |
python scripts/main.py --input-dir /data --output manifest.json --recursive
python scripts/main.py --input-dir /data --output manifest.csv --format csv
For research use only. This skill is not intended for clinical use.
Validates DICOM series consistency by checking SeriesInstanceUID, ImageOrientationPatient, slice spacing uniformity, and detecting missing slices.
Normalizes medical image intensities using various methods (z-score, min-max, percentile, window-level). Essential preprocessing step for consistent analysis across different scanners and protocols.
Generate research-oriented HTML or Markdown case reports for volumetric medical images, including summary statistics and slice montages.
Resample NIfTI medical images to target spacing with configurable interpolation methods and optional anti-aliasing for downsampling.
Generates a complete MONAI-based training scaffold for medical image segmentation or classification tasks. Creates training scripts, inference scripts, configuration files, data loading pipelines, and model definitions.
Validates NIfTI file headers for integrity and consistency. Checks header structure, dimension consistency, orientation matrix, data type support, and file size expectations.