A T1 MRI scan can be segmented to 133 labels with consistent cortical surface reconstruction.
It has been implemented as a single Docker.
- Please cite the following papers, if you used the MaCRUISE.The papers can be found MaCRUISE journal and MaCRUISE conference, whole full citation is
Yuankai Huo, Andrew J. Plassard, Aaron Carass, Susan M. Resnick, Dzung L. Pham, Jerry L. Prince, and Bennett A. Landman. "Consistent cortical reconstruction and multi-atlas brain segmentation." NeuroImage 138 (2016): 197-210.
Yuankai Huo, Aaron Carass, Susan M. Resnick, Dzung L. Pham, Jerry L. Prince, and Bennett A. Landman. "Combining multi-atlas segmentation with brain surface estimation." In Medical Imaging 2016: Image Processing, vol. 9784, p. 97840E. International Society for Optics and Photonics, 2016.
+ The code and docker are free for noncommercial purposes.
+ The licence.md shows the terms for commercial and for-profit purposes.Get our docker images, one is for SLANT whole brain segmentaiton, another one is for MaCRUISE
# Get SLANT docker image (~5G disk space)
sudo docker pull vuiiscci/slant:deep_brain_seg_v1_0_0
# Get MaCRUISE docker image (~10G disk space)
sudo docker pull masidocker/spiders:MaCRUISE_v3_1_0
Step 1, Run SLANT brain segmentation (will take ~ 15 mins)
You can run the following command or change the "input_dir", then you will have the final segmentation results in output_dir
# you need to specify the input and output directory
export input_dir=/home/input_dir
export output_dir=/home/output_dir
# make SLANT directory
export SLANT_input_dir=$input_dir/SLANT
export SLANT_output_dir=$output_dir/SLANT
sudo mkdir $input_dir
sudo mkdir $SLANT_input_dir
sudo mkdir $output_dir
sudo mkdir $SLANT_output_dir
# download the test volume file, you can even put multiple input files here, no worries.
sudo wget -O $SLANT_input_dir/test_volume.nii.gz https://www.nitrc.org/frs/download.php/10666/test_volume.nii.gz
# run the docker
sudo nvidia-docker run -it --rm -v $SLANT_input_dir:/INPUTS/ -v $SLANT_output_dir:/OUTPUTS masidocker/spiders:deep_brain_seg_v1_0_0 /extra/run_deep_brain_seg.sh
You can run the following command or change the "input_dir", then you will have the final segmentation results in output_dir
# make MaCRUISE directory
export MaCRUISE_input_dir=$input_dir/MaCRUISE
export MaCRUISE_output_dir=$output_dir/MaCRUISE
sudo mkdir $MaCRUISE_input_dir
sudo mkdir $MaCRUISE_output_dir
# prepare files
cp $SLANT_input_dir/test_volume.nii.gz $MaCRUISE_input_dir/T1.nii.gz
cp $SLANT_output_dir/FinalResult/test_volume_seg.nii.gz $MaCRUISE_input_dir/orig_target_seg.nii.gz
# run the docker
sudo docker run --rm -v $MaCRUISE_input_dir:/INPUTS/ -v $MaCRUISE_output_dir:/OUTPUTS/ masidocker/spiders:MaCRUISE_v3_1_0 xvfb-run -a --server-args="-screen 0 1920x1200x24 -ac +extension GLX" /extra/MaCRUISE_v3_1_0
- You will see the final egmentation file in "../MaCRUISE/MaCRUISE/Output/SegRefine"
- You will see the final surface file in "../MaCRUISE/MaCRUISE/Output/Surfaces" and "../MaCRUISE/MaCRUISE/Output/Surfaces_FreeView"
- You will see the final a overlay pdf in "../MaCRUISE/MaCRUISE/Output/PDF"
The source code have all been contained in the Docker
- Ubuntu 16.04
- cuda 8.0
- Pytorch 0.2
- Docker version 1.13.1-cs9
- Nvidia-docker version 1.0.1 to 2.0.3
sudo apt-get install apt-transport-https ca-certificates curl software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
sudo apt-get update
sudo apt-get install docker-ce
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update
sudo apt-get install -y nvidia-docker2
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