How to use the Tensorflow code?

• Download the iSEG-2017 data and place it in data folder. (Visit this link to download the data. You need to register for the challenge.)
• To perform image wise normalization and correction( run preprocess_static once more by changing dataset argument from "labeled" to "unlabeled"):

$ cd preprocess
$ python
>>> from preprocess import preprocess_static
>>> preprocess_static("../data/iSEG", "../data/iSEG_preprocessed",dataset="labeled")

• The preprocessed images will be stored in iSEG_preprocessed folder
• If you fail to install ANTs N4BiasFieldCorrection you can also skip the above preprocessing step and continue working with the original dataset. (Just change the data_directory flag to the original data directory while running the models)
• You can run standard 3D U-Net & our proposed model(Feature matching GAN, weighted Feature Matching GAN and bad GAN) with this code and compare their performance.

How to run 3D U-Net?

$ cd ../unet3D

• Configure the flags according to your experiment.
• To run training

$ python main_unet.py --training

• This will train your model and save the best checkpoint according to your validation performance.
• You can also resume training from saved checkpoint by setting the load_chkpt flag.
• You can run the testing to predict segmented output which will be saved in your result folder as ".nii.gz" files.
• To run testing

$ python main_unet.py --testing

• This version of code only compute dice coefficient to evaluate the testing performance.( Once the output segmented images are created you can use them to compute any other evaluation metric)
• Note that the U-Net used here is modified according to the U-Net used in proposed model.(To stabilise the GAN training)
• To use the original U-Net you need to change the replace network_dis with network(both networks are provided) in build_model function of class U-Net(in model_unet.py).

How to run GAN based 3D U-Net?

$ cd ../proposed_model

• Configure the flags according to your experiment.
• To run training

$ python main.py --training

• By default it trains Feature Matching GAN based model. To train the weighted Feature Matching GAN based model

$ python main.py --training --use_weighted_fm

• To train the bad GAN based model

$ python main.py --training --badGAN

• To run testing

$ python main.py --testing

• Once you run both the model you can compare the difference between their perfomance when 1 or 2 training images are available.