菜单
Generate research-oriented HTML or Markdown case reports for volumetric medical images, including summary statistics and slice montages.
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基于 SOC 职业分类
Scan medical imaging directories and generate structured JSON or CSV manifests with file-level metadata for DICOM and NIfTI assets.
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.
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.
| name | medical-image-case-report |
| description | Generate research-oriented HTML or Markdown case reports for volumetric medical images, including summary statistics and slice montages. |
| version | 1.0.0 |
| author | Medical AI Skills Team |
| license | MIT |
| tags | ["reporting","visualization","nifti","case-report"] |
| maturity | experimental |
| category | reporting |
| requires_extras | ["core","reporting"] |
This skill generates a research-facing case report from a volumetric medical image and, optionally, a segmentation mask. It summarizes image dimensions, spacing, intensity statistics, and slice visualizations.
pip install .[reporting]
Dependencies:
python scripts/main.py IMAGE_PATH [OPTIONS]
| Argument | Required | Description |
|---|---|---|
IMAGE_PATH | Yes | Path to an input medical image file |
| Option | Default | Description |
|---|---|---|
--mask | none | Optional segmentation mask path |
--output | report.html | Output report file |
--format | html | Output format: html or md |
--slices | 5 | Number of montage slices |
--verbose | false | Enable verbose logging |
--version | - | Show version and exit |
--help | - | Show help and exit |
Input is a single medical image file readable by the implementation and an optional mask file aligned to the image grid.
{
"success": true,
"result": {
"report_path": "report.html",
"summary_path": "report.json",
"format": "html",
"has_mask": true
},
"metadata": {
"timestamp": "2024-01-15T10:30:00Z",
"duration_ms": 1234,
"version": "1.0.0"
}
}
| Code | Meaning |
|---|---|
| 0 | Success |
| 1 | Validation error |
| 2 | Runtime error |
| 3 | Usage error |
python scripts/main.py patient_001.nii.gz --output case_report.html
python scripts/main.py scan.nii.gz --mask lesion_mask.nii.gz --format md
For research use only. This skill is not intended for clinical use.