HABIT Documentation

HABIT turns images + an ROI into habitat maps, then quantifies those habitats. Supporting image preprocessing is provided for data ingestion.

Warning

Research / education only. Not for clinical diagnosis.

Start here

1. Installation — install

2. Quickstart: Python API — first habitat map (Python)

3. Quickstart: YAML — load that analysis from YAML

4. Quickstart: run the demo (YAML + CLI) — first habitat map (CLI)

5. Habitat Guide — Habitat Guide (one task per page)

Stuck: FAQ. Signatures and defaults: API Reference. Scientific definitions: Features from habitat maps.

Get started

• Installation
• Quickstart: Python API
• Quickstart: YAML
• Quickstart: run the demo (YAML + CLI)

Habitat Guide

• Habitat Guide

Reference

• API Reference
  • Habitat analysis pipeline
  • Supporting bookmarks
  • Narrative guides
• Habitat Spec component catalog
  • How a HabitatSpec is assembled
  • Python and YAML are the same document
  • 1. Voxel feature extraction
  • 2. Feature preprocessing
  • 3. Supervoxel partition
  • 4. Supervoxel features
  • 5. Pool
  • 6. Fit habitats
  • 7. Assign
  • 8. Quantify
  • What to read next
• Features from habitat maps
  • MSI Features
  • ITH Score
  • Graph topology features
  • Non-radiomics Morphology
  • Traditional Radiomics
  • Whole / Each Habitat Radiomics
• Matching habitat labels across fits and subjects
  • When matching is needed
  • Choosing a method
  • Why a single reference is not enough
  • Prototype matching (cohort)
  • Assumptions and limitations
  • Usage
  • References
• CLI and YAML
  • Configuration recipe catalog
  • Habitat Segmentation Configuration
  • Feature Extraction Configuration
  • Traditional Radiomics Configuration
  • Preprocessing Configuration
  • DICOM Sort Configuration
  • Auxiliary Tools and Data Configuration
  • Supporting bookmarks
• Command reference
• FAQ
  • Install
  • Run
  • Data
• Upstream Dependencies and Documentation Links
  • Image preprocessing (habit preprocess)
  • Habitat segmentation (habit get-habitat)
  • Habitat features and traditional radiomics (habit extract / habit radiomics)
  • Machine learning (habit model / habit cv)
  • Model comparison (habit compare)
  • ICC / Test–retest / other
  • ROI delineation (external tools)

Developer

• Architecture
• Contributing
• Extending HABIT

Changelog

• Changelog

Acknowledgments

• Acknowledgments

• GitHub: https://github.com/lichao312214129/HABIT