soudipd/3d-unet-keras-brats2019

3D Unet biomedical segmentation model for BraTS2019

3DUnet-keras

3D Unet biomedical segmentation model powered by tensorpack with fast io speed.

Borrow a lot of codes from https://github.com/tkuanlun350/3DUnet-Tensorflow-Brats18. I streamlined the code and changed it to the keras version.

I want to verify the effectiveness (consistent improvement despite of slight implementation differences and different deep-learning framework) of some architecture proposed these years. Such as dice_loss, generalised dice_loss, residual connection, instance norm, deep supervision ...etc. Those design are popular and used in many papers in BRATS competition.

Dependencies

• Python 3;
• TensorFlow 1.12.0;
• Keras 2.2.4;

• (Optional) If you want to use Bias Correction you have to install nipype and ANTs (see preprocess.py)

DIR/
  training/
    HGG/
    LGG/
  val/
    BRATS*.nii.gz

Data

Usage

Change config in config.py:

1. Change BASEDIR to /path/to/DIR as described above.

Train:

python3 train.py --logdir=./train_log/unet3d --gpu 0

Eval:

python3 train.py --load=./train_log/unet3d/model-30000 --gpu 0 --evaluate

Predict:

python3 train.py --load=./train_log/unet3d/model-30000 --gpu 0 --predict

If you want to use 5 fold cross validation :

1. Run generate_5fold.py to save 5fold.pkl
2. Set config CROSS_VALIDATION to True
3. Set config CROSS_VALIDATION_PATH to {/path/to/5fold.pkl}
4. Set config FOLD to {0~4}