Transfer Learning for Brain Tumor Segmentation

This is the repository containing the code to reproduce the experiments in Transfer Learning for Brain Tumor Segmentation (arXiv: http://arxiv.org/abs/1912.12452).

Requirements to run the code:

• BraTS '17/'18 Training Data and BraTS '18 Validation Data -- See here for instructions how to obtain the data: http://braintumorsegmentation.org/ -- The BraTS '18 validation data is used to obtain test results. Predictions can of course be carried out on any other dataset.
• PyTorch
• batchgenerators by MIC@DKFZ (https://github.com/MIC-DKFZ/batchgenerators) -- Follow the following guide in order to convert the BRATS data into the necessary format: -- https://github.com/MIC-DKFZ/batchgenerators/tree/master/batchgenerators/examples/brats2017 -- The data should be stored in brats_data_preprocessed/Brats17TrainingData (and Brats18ValidationData) -- Be aware that the new output format occupies considerably more disk space! E.g. 18 GB for the BraTS 17 training data!

Files and folders inside this repository:

• brats_data_preprocessed: The preprocessed BraTS data stored in a separate subdirectory for each year and type (train/validation)

• models: The models saved by PyTorch

• segmentation_output: The output segmentations produced by the trained model in NIFTI format. These can be directly uploaded to the BraTS evaluation server.

• tensorboard_logs: Tensorboard logfiles that contain the dice scores/losses over time.

• Read-Logs.ipynb: Notebook to visualize the tensorboard logs

• Dice-Plots.ipynb: Notebook to visualize the dice box plots

• Seg-Graphic.ipynb: Notebook to visualize the example patient segmentation

• brats_data_loader.py: Wrapper class for the BraTS dataloader used to train the model from the preprocessed files.

• jonas_net.py: Contains the AlbuNet3D architecture using a ResNet34 encoder.

• tb_log_reader.py: Wrapper class to read tensorboard logs.

• ternaus_unet_models.py: Reference file containing the original AlbuNet model.

• train_jonas_net_batch.py: Python script to train the model for a given configuration passed as arguments.

• train_test_function.py: Helper class to facilitate the training procedure for any deep learning model.

• run_experiments_x.sh: Shell script to launch train_jonas_net_batch.py for the configurations used in the paper.