README.md

Deepmedic and 3D U-Net for brain tumor segmentation

We created two popular deep learning models DeepMedic and 3D U-Net in PyTorch for the purpose of brain tumor segmentation.

For more details about our methodology, please refer to our paper

The performance of our proposed ensemble on BraTS 2018 dataset is shown in the following table:

Dataset	Dice(ET)	Dice(WT)	Dice(TC)	Hausdorff95(ET)	Hausdorff95(WT)	Hausdorff95(TC)
Training	0.735	0.902	0.813	5.433	5.398	6.932
Validation	0.788	0.905	0.813	3.811	4.323	7.553
Testing	0.749	0.875	0.793	4.219	6.479	6.522

For the training set, we use 5-folds cross validation. ET:Enhancing tumor, WT: whole tumor, TC: tumor core.

Citation

The system was employed for our research presented in [1,2], where the we integrate multiple DeepMedics and 3D U-Nets in order to get a robust tumor segmentation mask. We also utilize the brain parcellation masks for the purpose of bringing the location information to DeepMedic and 3D U-Net. If the use of the software or the idea of the paper positively influences your endeavours, please cite [1,2].

[1] Po-Yu Kao, Thuyen Ngo, Angela Zhang, Jefferson Chen, and B. S. Manjunath, "Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction." International MICCAI Brainlesion Workshop. Springer, Cham, 2018.

[2] Po-Yu Kao, Shailja Shailja, Jiaxiang Jiang, Angela Zhang, Amil Khan, Jefferson W. Chen, and B. S. Manjunath, "Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information" Front. Neurosci. 13:1449. doi: 10.3389/fnins.2019.01449

Dependencies

Python3.6

Pytorch0.4.0

pip install git+https://github.com/pykao/pytorch-v0.4.0@pytorch_0.4.0_poyu

Install custom pytorch kernels from https://github.com/thuyen/multicrop

pip install git+https://github.com/thuyen/multicrop.git

Required Python libraries

nibabel, nipype, natsort, SimpleITK

pip install nibabel,nipype,natsort,SimpleITK

Required Sofware

FSL (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation)

Existing Brain Parcellation in Subject Space for BraTS2018 dataset

The Harvard-Oxford subcortical atlases in subject space are stored at BrainParcellation/HarvardOxford-sub

Create the HarvardOxford Subcortical Brain Parcellation to Subject Space for New BraTS Subject

python createBrainParcellation.py -i /DIR_TO_MR_T1.nii.gz -o /DIR_TO_SAVE_THE_BRAIN_PARCELLATION -n SUBJECT_NAME

The output brain parcellation will be named as SUBJECT_NAME_HarvardOxford-sub.nii.gz

Using Brain Parcellation on DeepMedic and 3D U-Net

For using the brain parcellation on DeepMedic and 3D U-Net, please change the paths in data/parcellation.py accordingly

How to run

Change:

experiments/settings.yaml

to point to data directories. These are general settings, applied to all experiments. Additional experiment-specific configuration will overwrite these.

Split data to 5 fold train/valid splits:

python split.py

Preprocess data (look at the script for more details):

python prep.py

Prepare parcellation data:

For using the brain parcellation for DeepMedic and 3D U-Net, please change the paths in data/parcellation.py accordingly

python data/parcellation.py

For standard DeepMedic, run:

python train.py --gpu 0 --cfg deepmedic_ce

For DeepMedic with 12-by-12-by-12 output mask, run:

python train_12.py --gpu 0 --cfg deepmedic_ce_28x20x12

For DeepMedic with 6-by-6-by-6 output mask, run:

python train_6.py --gpu 0 --cfg deepmedic_ce_22X18X6

For 3D U-Net run:

python train_unet.py --gpu 0 --cfg unet_dice2

Prediction

To make predictions, run predict.py, predict_6.py, predict_12.py or predict_unet.py with similar arguments

Submission

To make submissions, look at make_submission.py

Memory Issue (Data Compression)

If you do not have enough memory to save the output probability maps, you are able to compress these maps to uint16 format by compress_data.py

To make submissions with compressed probability maps, please refer to ensemble_methods.py

Special thanks to Thuyen Ngo.