LoserSun/PartiallyReversibleUnet

Partially Reversible U-Net Code

Public PyTorch implementation for our paper A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation, which was accepted for presentation at MICCAI 2019.

If you find this code helpful in your research please cite the following paper:

@article{PartiallyRevUnet2019Bruegger,
         author={Br{\"u}gger, Robin and Baumgartner, Christian F.
         and Konukoglu, Ender},
         title={A Partially Reversible U-Net for Memory-Efficient Volumetric Image Segmentation},
         journal={arXiv:1906.06148},
         year={2019},

If you want to create your own reversible or partially reversible neural network you can use our RevTorch library.

Virtual Environment Setup

The code is implemented in Python 3.6 using PyTorch 1.1.0. Follow the steps below to install all dependencies:

• Set up a virtual environment (e.g. conda or virtualenv) with Python 3.6
• Install all non-PyTorch requirements using: pip install -r dataProcessing/brats18_data_loader.py
• Install PyTorch following the instructions on ther website.

Data

We trained with the BraTS 2018 dataset, which is available from the organizers of the BraTS challenge.

To prepare the data, adjust the paths at the end of dataProcessing/brats18_data_loader.py. Then run this script. Do the same for dataProcessing/brats18_validation_data_loader.py, which prepares the validation data.

Running the code

• Adjust the path in systemsetup.py to match your system.
• Run the train.py script

The settings for the experiments are each in an individual file located in the experiments/ folder. You can change the experiment by importing a different experiment in the file segmenter.py.

Creating checkpoits and prediction

• To create a checkpoint after every epoch, set SAVE_CHECKPOINTS = True in the corresponding experiment file
• For inference, you need to load a checkpoint form a previously trained model. To achieve this, set the following three fields in the corresponding experiment file:

PREDICT = True
RESTORE_ID = <Id to load>
RESTORE_EPOCH = <Epoch to load>

Running train.py now will run inference for all images in the validation dataset.