TAPS
TAPS deploys a trained prostate MRI segmentation model as a Python package and command-line application. It accepts a NIfTI image and writes a binary NIfTI mask aligned with the original input image.
See https://github.com/laviolette-lab/TAPS-Training-Code for the original training code used to create this model and write the paper.
Install
pip install lavlab-taps
The installation includes PyTorch, MONAI, NumPy, and NiBabel. Install a CUDA-compatible PyTorch build first when GPU inference is required.
CLI
taps segment input_image.nii.gz prostate_mask.nii.gz
TAPS uses the bundled checkpoint by default and selects CUDA automatically when it is available. Override either with --checkpoint and --device:
taps segment input_image.nii.gz prostate_mask.nii.gz \
--checkpoint best_segresnet_model.pth --device cpu
The package applies the validation preprocessing pipeline, performs sliding-window inference, then restores the prediction to the source image's voxel space before saving it.
Python API
from taps import segment
segment(
"input_image.nii.gz",
"prostate_mask.nii.gz",
checkpoint=None, # use the bundled model
)
Project Structure
taps/
├── src/
│ └── taps/
│ ├── __init__.py # Public API & version export
│ ├── __about__.py # Version string
│ ├── cli.py # CLI entry point (thin wrapper)
│ ├── inference.py # Preprocessing, model loading & segmentation
│ ├── py.typed # PEP 561 marker
│ └── resources/
│ └── model_v1.pth # Bundled checkpoint
├── tests/
├── docs/ # MkDocs source files
└── pyproject.toml
Development
Prerequisites: Python 3.9+ and Hatch.
git clone https://github.com/laviolette-lab/taps.git
cd taps
pip install hatch
| Task | Command |
|---|---|
| Run tests | hatch run test:test |
| Tests + coverage | hatch run test:cov |
| Lint | hatch run lint:check |
| Format | hatch run lint:format |
| Auto-fix lint | hatch run lint:fix |
| Type check | hatch run types:check |
| Build docs | hatch run docs:build-docs |
| Serve docs | hatch run docs:serve-docs |
| Build wheel | hatch build |
Or via the Makefile: make test, make lint, make build, etc.
Docker
# Run tests via Docker
docker build --target hatch -t taps:hatch .
docker run --rm -e HATCH_ENV=test taps:hatch cov
# Production image (just the installed wheel)
docker build --target prod -t taps:prod .
Contributing
See CONTRIBUTING.md for development guidelines.
License
taps is distributed under the terms of the MIT license.
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