This is an implementation of the paper Cross-Modality Deep Feature Learning for Brain Tumor Segmentation published on Pattern Recognition. The "2m_re1" program is for BraTS 2018.

Environment

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python(Tested on 3.6)
pytorch(Tested on 0.4.1)

Data Set

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BraTS 2017 data set(including training set and validation set).
BraTS 2018 data set(including training set and validation set).
Sample list file *.txt(eg. train.txt), which contains the filenames of the inputs.

Preprocessing

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Before training or testing, a preprocessing is needed to generate proper data format for the implementation.

• training data

python run_preprocessing.py --mode=train

• validation data

python run_preprocessing.py --mode=val

Test

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Change the "run_mode" to "test" in the code file seg2g.py and run:

python seg2g.py

Post processing

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Change the file directories to your local data directories in the code file seg_post.py and run:

python seg_post.py

Train

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The training of this implementation is divided into two stages. The first stage is to train CycleGAN network, and the second stage is to train the segmentation network based on transfer learning.

• The first stage

python gan.py

• The second stage, set the run the "run_mode" to "train" and run:

python seg2g.py