A 2D and 3D PyTorch implementation of the Tiramisu CNN
Project description
tiramisù-brûlée
A 2D and 3D PyTorch implementation of the Tiramisu CNN
This package is primarily used for multiple sclerosis (MS) lesion segmentation; specifically, T2 lesions in the brain.
Free software: Apache Software License 2.0
Documentation: https://tiramisu-brulee.readthedocs.io.
Install
The easiest way to install the package is with:
pip install tiramisu-brulee
Alternatively, you can download the source and run:
python setup.py install
If you want a CLI to train a lesion segmentation model (or work with anything in the experiment subpackage), install with:
pip install "tiramisu-brulee[lesionseg]"
Basic Usage
Import the 2D or 3D Tiramisu version with:
from tiramisu_brulee.model import Tiramisu2d, Tiramisu3d
If you install tiramisu-brulee with [lesionseg] extras, then you can train a lesion segmentation Tiramisu CNN and predict with:
lesion-train ... lesion-predict ... lesion-predict-image ...
Use the --help option to see the arguments. See the documentation for a tutorial on how to use the CLIs.
References
[1] Jégou, Simon, et al. “The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation.” CVPR. 2017.
[2] Zhang, Huahong, et al. “Multiple sclerosis lesion segmentation with Tiramisu and 2.5D stacked slices.” International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 2019.
Why the name?
Why is the name tiramisù-brûlée? Well, tiramisù is named after the neural network [1] whose name is inspired by the dessert; however, tiramisu—by itself—was already taken as a package on PyPI. I added brûlée to get around the existence of that package and because this package is written in PyTorch (torch -> burnt). Plus brûlée in English is often associated with the dessert crème brûlée. Why combine an Italian word (tiramisù) with a French word (brûlée)? Because I didn’t think about it until after I already deployed the package to PyPI.
History
0.1.9 (2021-05-28)
Add pseudo3d (2.5D) support and patch-based prediction
0.1.8 (2021-05-27)
Fix ISBI 15 score metric
0.1.7 (2021-05-25)
Add precision to arguments for prediction
0.1.6 (2021-05-25)
Improve documentation
0.1.5 (2021-05-25)
Add docs and split out CLIs from seg module
0.1.4 (2021-05-13)
Add lesion segmentation CLI.
0.1.3 (2021-05-13)
Fix deployment by fixing repo name in travis.
0.1.2 (2021-05-13)
Fix supported versions and docs.
0.1.1 (2021-05-13)
Fix tests and deployment.
0.1.0 (2021-05-13)
First release on PyPI.
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