Med-TDA Documentation
Welcome to Med-TDA (Medical Imaging Topological Data Analysis), a comprehensive Python library for extracting TDA features from medical images. Med-TDA provides a complete pipeline from image preprocessing to persistence barcode computation and feature vectorization, designed specifically for medical imaging applications in machine learning and radiomics research.
Key Features
🎯 Easy-to-use: Simple scikit-learn-style API with sensible defaults
🔧 Flexible: Support for 2D, 3D, and 4D medical images with comprehensive preprocessing
⚡ Efficient: Optimized persistent homology computation using cripser and GUDHI
📊 Comprehensive: 8 vectorization methods for different use cases
🏥 Medical Image Support: Built-in support for NIfTI, NRRD, MHA, MHD, and common 2D formats
💻 CLI Support: Command-line interface for batch processing and integration with pipelines
⚙️ Configurable: YAML configuration files with extensive parameter control
Quick Example
Extract TDA features from a medical image in just a few lines:
from medtda import FeatureExtractor
# Initialize with desired settings
extractor = FeatureExtractor(
normalize=True,
vectorization_method='PersStats'
)
# Extract features
features = extractor.execute('image.nii.gz', 'mask.nii.gz')
# Use features in your ML pipeline
print(f"Extracted {len(features)} TDA features")
Or use the command-line interface:
# Single file processing
medtda image.nii.gz --mask mask.nii.gz --output-dir ./results --normalize
# Batch processing with parallel workers
medtda cases.csv --output-dir ./results --workers 4 --verbose
Getting Started
User Guide
Comprehensive guides for using MedTDA:
API Reference
Detailed API documentation for all modules and classes:
TDA Theory
Background on topological data analysis concepts:
Examples
Practical examples and tutorials: