brats/ucsbvrl repository overview

This repository is hosted by the BraTS organizers with permission by the original author. Input format: b17-gz

This repository contains the brain tumor segmentation algorithm used in Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction. This is a GPU ONLY version and the minimum requirement for the GPU memory is 11 GB. This may take around 20 minutes to process a BraTS subject. The input data files, i.e., the ‘flair.nii.gz’, ‘t1c.nii.gz’, ‘t1.nii.gz’, ‘t2.nii.gz’ volumes, will be linked to /data and your segmentations are placed in /data/results. The outputs including UCSBVRL_HarvardOxford-sub.nii.gz, HarvardOxford Subcortical atlas in subject space, and tumor_UCSBVRL_class.nii.gz, the predicted tumor mask.

Please cite us [1] if you find this repository is helpful to your research.

[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." arXiv preprint arXiv:1807.07716, 2018.

How to run:

docker run --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=0 --shm-size 11G -v <host_directory>:/data -it pykao/brats2018:latest