This is our solution used for the participation of MICCAI BraTS 2019 competition. We were lucky and won a third place award in the second task. For more information about tasks in BraTS 2019 please refer to their homepage.

For more details please refer to the International MICCAI Brainlesion Workshop paper: "3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction"

We've only touched the segmentation task(task1) and the survival task(task2).

The 3D U-Net model is borrowed from Isensee et.al's paper and ellisdg's repository. You could also see this implementation as an extension to ellisdg's work. The solution to task 2 is a development based on xf4j's work which won the title in BraTS 2018.

If you wanna download our trained models, there is an image we uploaded to dockerhub inside which you could find the saved .h5 files.

Both my desktop and laptop had contributed a lot to the project.

Desktop:

• gtx1080ti
• ubuntu16.04 virtualenv python==3.5.2 tensorflow-gpu==1.11.0 keras==2.2.4

Laptop:

• gtx1060
• ubuntu16.04 virtualenv python==3.5.2 tensorflow-gpu==1.14.0 keras==2.2.4

You could refer to my setting up history here

Here are some packages you may need to install.

• For n4itk bias correction preprocessing, you may need to install ants.
  • Just follow the installation guide on their homepage here
  • Add ants to your environment variable PATH, for instance like $ export PATH=${PATH}:/usr/lib/ants/

• $ sudo apt-get install libhdf5-serial-dev 
  $ pip install tables
  

• $ pip install numpy, nibable, SimpleITK, tqdm, xlrd, pandas, progressbar, matplotlib, nilearn, sklearn
  

• For Instance Normalization, you may need to download and install keras-contrib.

  $ git clone https://www.github.com/farizrahman4u/keras-contrib.git
  $ pip install <where you saved it>
  

For other missed packages you may come across, just install them as required according to the ImportError.

original_tree.txt shows the original organization of this whole project before you start the training process.

data/original saves training dataset. data/val_data saves validation or test dataset. data/survival_data.csv is the phenotypic information for training subjects. data/val_data/val/survival_evaluation.csv is the phenotypic information for validation or test subjects. data/preprocessed and data/preprocessed_val_data saves the dataset after preprocessing procedure.

Folder dev_tools provides some of my own functions in common use.

Folder unet3d encapsulates 3D U-Net related functions that you could invoke in different demos.

demo_task1 includes brain tumor segmentation task specific codes.

demo_task2 includes survival days prediction task specific codes.

The self-explained demo_run.ipynb in demo_task1 and demo_task2 illustrate the basic flow of the program. Since there are two phases of training process with different patching strategies, we need to switch the bool value of config['pred_specific'] in demo_task1/train_model.py to decide which strategy do we need to use for training. You may also need to manually delete two temporarily generated file demo_task1/num_patches_training.npy and demo_task1/num_patches_val.npy once you changed the patching strategy.

This program is valid for both validation dataset and test dataset. Once you changed the validation dataset (to test dataset), please delete the old data/val_data.h5 and data/val_index_list.pkl.

The n4itk bias correction is time consuming and optional during this project. You could manually turn it off by means of setting the certain argument during the preprocessing process. If you don't use the bias correction then you may not need to install ants at the beginning of demo_task1/demo_run.ipynb.

Last but not the least, pay attention that we shield something in .gitignore.

Again, this work refers to Isensee et.al's paper, ellisdg's repository and xf4j's repository. We deeply appreciate their contributions to the community.

Many thanks to the host of BraTS 2019.