xiaochengcike / brats2018-graphlabunn

Cascaded UNet for brain tumor segmentation

This code was written for participation in the Brain Tumor Segmentation Challenge (BraTS) 2018.
The code is based on the corresponding paper, where we present deep cascaded approach for automatic brain tumor segmentation. We modify 3D UNet architecture and implement specific augmentation strategy to efficiently handle multimodal MRI input, besides this we introduce approach to enhance segmentation quality with context obtained from models of the same topology operating on downscaled data.
Each input modality has its own encoder which are later fused together to produce single output segmentation.

Network achitecture

Structure of the residual block

Cascading strategy

The cascading approach is also employed. The details can be found in the paper.

• Dmitry Lachinov, Evgeny Vasiliev, and Vadim Turlapov. "Glioma Segmentation with Cascaded Unet." arXiv preprint arXiv:1810.04008 (2018).

Data	WT	ET	TC
Val 2018	0.906	0.784	0.844
Test 2018	0.878	0.720	0.795

How to train

1. Download BraTS Dataset from the brats website
2. Edit the config
   1. open scripts/config.py
   2. edit variables from the Config section.
3. Run script preprocess_and_train.sh (to run both augmentation and training)

How to validate

Run the following command. Dataset and models root directories will be taken from config.

python validate.py --name <model's name>

How to perform prediction

Run the following command. Models root directory will be taken from config.

python predict.py --name <model's name> --test_data_dir <dir with test data> --out_data_dir <output dir> --is_single_series(optional)

--is_single_series indicates whether test_data_dir has only single series.

Config

• data_directory - path to dataset root directory, should contain both HGG and LGG subdirectories
• augumented_directory - the output of augmentation procedure, should be initially empty
• models_root_path - path where the models will be stored
• segmentation_model_name - name of the segmentation model
• dataset_multiplier - number of augmentation runs. More this number is, more augmentation circles will be performed

Train

cd scripts  
sh preprocess_and_train.sh <available port>

Run

python predict.py --name <model_name> --test_data_dir <input directory> --out_data_dir <output directory>

Requirements

python 2.7
numpy
mxnet-cu90
scikit-learn
scikit-image
scipy
nibabel
tqdm
SimpleITK
itk
opencv-python
mxboard
matplotlib
tensorboard

Docker

This model can be trained in the docker. Corresponding Dockerfile can be found in the repo root. Training can be run with the following command:

nvidia-docker run --rm -t --name ${EXPERIMENT_NAME} \
  --user=$(id -u):$(id -g) $ADD_GROUPS \
  -v <path to dataset root>:/dataset \
  -v <path to models root>:/models \
  -v <path to scripts>:/scripts \
  -v ../compute_cache/:/cache \
  -p ${PORT}:${PORT}\
  -e CUDA_CACHE_MAXSIZE=2147483647\
  -e CUDA_CACHE_DISABLE=0\
  -e CUDA_CACHE_PATH="/cache" \
  $IMAGE_NAME bash -c "cd /scripts/ && ./preprocess_and_train.sh ${PORT}"