Medical Imaging Pipelines
OfficialAutomate medical imaging data workflows.
Education & Research#machine learning#data pipeline#preprocessing#dicom#format conversion#medical imaging#nifti
Authoraurabx
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill streamlines the complex and time-consuming process of preparing medical imaging data for analysis, machine learning, and research.
Core Features & Use Cases
- Format Conversion: Convert DICOM to NIfTI, PNG, JPEG, or HDF5.
- Preprocessing: Apply intensity normalization, resampling, cropping, and padding.
- Metadata Extraction: Generate manifests and statistics from DICOM studies.
- Use Case: Prepare a large dataset of CT scans for a deep learning model by converting them to NIfTI, normalizing HU values, and resampling to isotropic spacing.
Quick Start
Use the Medical Imaging Pipelines skill to convert all DICOM files in the '/data/dicom_study' directory to PNG images in '/data/png_output'.
Dependency Matrix
Required Modules
pydicomnumpySimpleITKnibabelpillowscikit-imagescipyh5pypandastqdmpylibjpegpylibjpeg-libjpeg
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: Medical Imaging Pipelines Download link: https://github.com/aurabx/skills/archive/main.zip#medical-imaging-pipelines Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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