Welcome to fastrad’s documentation!

FastRadiomics (fastrad) is a GPU-accelerated Python library for calculating radiomics features from medical images.

It aims to be a high-performance, strictly-compliant alternative to PyRadiomics, built from the ground up to achieve identical feature extraction parity with significantly faster execution times by utilizing PyTorch for large-scale tensor operations.

User Guide

Documentation

• Introduction
  • Why fastrad?
  • Use Cases
• Installation
  • Prerequisites
  • Installing from Source
  • Hardware Acceleration (CUDA)
• Quickstart: Evaluating a Lung Nodule
  • Prerequisites
  • Step 1: Load the Clinical Volume
  • Step 2: Configure the Extractor Blueprint
  • Step 3: Execute the Extraction
  • Step 4: Consume the Feature Output
  • Next Steps
  • Advanced: Hardware-Accelerated Voxel-wise Feature Extraction
• User Guide
  • Core Philosophy: PyTorch over Sequences
  • Object-Orientated Architecture
  • Extraction Workflow Execution
• Learn (Examples)
  • 01: Basic Feature Extraction
  • 02: PyTorch CUDA GPU Acceleration Benchmark
  • 03: Multi-Patient Clinical Batch Processing
  • 04: Advanced Configurations & Fallback Logic
• Features & Mathematical Formulations
  • First Order Statistics
  • Shape (2D and 3D)
  • Gray Level Co-occurrence Matrix (GLCM)
  • Gray Level Size Zone Matrix (GLSZM)
  • Gray Level Dependence Matrix (GLDM)
  • Neighbourhood Gray Tone Difference Matrix (NGTDM)
• Benchmarks
  • IBSI Compliance and Numerical Parity
  • GPU Performance
  • CPU Performance
  • Optimizing GLSZM (cuCIM)
  • Stability Guarantee
  • Memory Footprint Optimization
  • Edge Case Handling
  • Dense Voxel-Wise Hardware Extraction Performance
• API Reference
  • MedicalImage
  • Mask
  • FeatureSettings
  • FeatureExtractor
  • DenseFeatureExtractor
• Frequently Asked Questions (FAQ)
  • Feature Extraction: Input, Customization, and Reproducibility
  • Hardware and Errors
  • Common Exceptions
• Developers Guide
  • Architecture: PyTorch over SimpleITK
  • Adding the Baseline (Feature Classes)
  • Scientific Parity Testing
  • Out-Of-Memory (OOM) GPU Catchers
• Contributing to fastrad
  • The PR Process and Gotchas
  • Submitting Bug Reports

Indices and tables

• Index

• Module Index

• Search Page