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.
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):
- Imaging (habitat / preprocess / feature extract):
preprocessed.zip(code 9bi3). After extract you must havedemo_data/preprocessed/images/anddemo_data/preprocessed/masks/next toconfig/(no nestedprocessed_images). If zip top level ispreprocessed/, extract intodemo_data/; ifimages/+masks/, put underdemo_data/preprocessed/. - Tabular ML (
habit model/habit cv):ml_data.zip(code atnp). Extract at project root to getdemo_data/ml_data/(e.g.breast_cancer_dataset.csv). If zip top level isml_data/, extract intodemo_data/.
Habitat-only demos need pack 1; add pack 2 only for ML demos. See Quickstart
- Imaging (habitat / preprocess / feature extract):
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 1.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| habitat_analysis-1.2.0.tar.gz | 1.6 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| habitat_analysis-1.2.0-cp310-cp310-win_amd64.whl | CPython 3.10 | CPython 3.10 | Windows x86-64 | Details |
Total release size: 3.9 MB
Release files / habitat_analysis-1.2.0.tar.gz
| Download URL | habitat_analysis-1.2.0.tar.gz |
|---|---|
| Size | 1.6 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/7.0.0 CPython/3.10.16
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Release files / habitat_analysis-1.2.0-cp310-cp310-win_amd64.whl
| Download URL | habitat_analysis-1.2.0-cp310-cp310-win_amd64.whl |
|---|---|
| Size | 2.2 MB |
| Tags | CPython 3.10 Windows x86-64 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.10.16
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