Skip to main content

Habitat Analysis: Biomedical Imaging Toolkit (HABIT)

Tumor habitat analysis and intratumoral heterogeneity quantification for clinical and radiomics research. Workflows are driven by YAML configs: preprocessing, habitat segmentation, feature extraction, and optional machine learning.

Language / 语言:English | 简体中文


Documentation

Online docs: https://lichao312214129.github.io/HABIT

Local build: cd docs && make html → docs/build/html/index.html

Suggested learning path

Step Topic Link
1 Install HABIT Installation
2 Demo workflow Quickstart
3 Step-by-step how-to How-to index
4 YAML parameters Configuration

Workflow chapters

Step Link
Prepare data Prepare data
Preprocessing Preprocess
Habitat segmentation Segment habitat
Feature extraction Extract features
Machine learning Train model
Model comparison Compare models
FAQ FAQ

Tools & more

Topic Link
CLI overview CLI reference
Contributing Contributing

Bundled config templates

After cloning or unpacking the repo, use the config/ folder at the project root (sibling to the habit/ Python package). See config/README_CONFIG.md and Configuration reference.


Install & demo data

Python 3.10–3.14. Full steps: Installation.

conda create -n habit python=3.10 -y
conda activate habit
pip install -U pip
pip install habitat-analysis -i https://pypi.org/simple
habit --version
# import name: import habit

Optional: napari for habit view (see Installation). PyRadiomics is separate when you need radiomics. Other capabilities are extras — missing ones raise OptionalDependencyError with the exact pip install command, e.g.:

pip install "habitat-analysis[ml,analysis]"
  • Source: GitHub (dev: pip install -e .)

  • Demo data (two packs):

    1. Imaging (habitat / preprocess / feature extract): preprocessed.zip (code 9bi3). After extract you must have demo_data/preprocessed/images/ and demo_data/preprocessed/masks/ next to config/ (no nested processed_images). If zip top level is preprocessed/, extract into demo_data/; if images/+masks/, put under demo_data/preprocessed/.
    2. Tabular ML (habit model / habit cv): ml_data.zip (code atnp). Extract at project root to get demo_data/ml_data/ (e.g. breast_cancer_dataset.csv). If zip top level is ml_data/, extract into demo_data/.

    Habitat-only demos need pack 1; add pack 2 only for ML demos. See Quickstart


Support & citation

  • Issues: GitHub Issues
  • Citation: see CITATION.cff
  • License: Apache License 2.0. Free for academic and commercial use; the only obligation is to retain the copyright and license notices and to ship NOTICE with redistributions. When HABIT supports scientific work, the authors request -- but do not require as a license condition -- that you cite it

Release files for habitat-analysis 1.1.3

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for habitat-analysis 1.1.3
File Size Uploaded
habitat_analysis-1.1.3.tar.gz 1.3 MB Details

Built distribution (wheel)

Table of built distributions (wheels) for habitat-analysis 1.1.3
File Interpreter ABI Platform
habitat_analysis-1.1.3-cp310-cp310-win_amd64.whl CPython 3.10 CPython 3.10 Windows x86-64 Details

Total release size: 3.1 MB

Release files / habitat_analysis-1.1.3.tar.gz

Download URL habitat_analysis-1.1.3.tar.gz
Size 1.3 MB
Tags Source
SHA-256 checksum
How to use checksums
c9a4980190f71827af9b8b3b525c28c6d2feb5e15350bd767b77e73c7b0ea3f3
BLAKE2b-256 checksum
How to use checksums
046908b89aa6f0254d8d54ac7391df6757bbc99d912638b8e0f47983379ab179
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.10.16

Release files / habitat_analysis-1.1.3-cp310-cp310-win_amd64.whl

Download URL habitat_analysis-1.1.3-cp310-cp310-win_amd64.whl
Size 1.8 MB
Tags CPython 3.10 Windows x86-64
SHA-256 checksum
How to use checksums
0e8b695423a29ce06b4291748354bec76d06a5f3404b49dcc9bf7441f3e97247
BLAKE2b-256 checksum
How to use checksums
d2620955b48338979e95a1568c17caa13607fbbf4eb5d4694c07a2773fb5f992
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.10.16

Release history Release notifications | RSS feed

3.0.0

2 release files

2.0.0

2 release files

1.2.0

2 release files

This release

1.1.3 This release

2 release files

1.1.2

2 release files

1.1.1

2 release files

1.1.0

2 release files

1.0.4

2 release files

1.0.3

2 release files

1.0.2

6 release files

1.0.1

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page