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satra/generate_dockerfile.sh

Created May 19, 2018 20:06
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  1. satra created this gist May 19, 2018.
    39 changes: 39 additions & 0 deletions generate_dockerfile.sh
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    docker run -it --rm kaczmarj/neurodocker:master generate docker \
    --base neurodebian:stretch \
    --pkg-manager apt \
    --install graphviz tree git-annex-standalone vim \
    emacs-nox nano less ncdu tig sed build-essential \
    libsm-dev libx11-dev libxt-dev libxext-dev libglu1-mesa \
    --freesurfer version=6.0.0-min \
    --ants version=b43df4bfc8 method=source cmake_opts='-DBUILD_SHARED_LIBS=ON' make_opts='-j 4'\
    --run 'ln -s /usr/lib/x86_64-linux-gnu /usr/lib64' \
    --miniconda \
    conda_install="python=3.6 pip jupyter cmake mesalib vtk pandas \
    matplotlib colormath nipype" \
    pip_install="datalad[full] duecredit" \
    env_name="simple2" \
    activate=true \
    --workdir /opt \
    --run 'mkdir -p /opt/data && cd /opt/data && \
    curl -sSL https://osf.io/download/rh9km/?revision=2 -o templates.zip && \
    unzip templates.zip && \
    rm -f /opt/data/templates.zip && \
    curl -sSL https://osf.io/download/d2cmy/ -o OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_OASIS-30_v2.nii.gz && \
    curl -sSL https://osf.io/download/qz3kx/ -o OASIS-TRT_brains_to_OASIS_Atropos_template.tar.gz && \
    tar zxf OASIS-TRT_brains_to_OASIS_Atropos_template.tar.gz && \
    rm OASIS-TRT_brains_to_OASIS_Atropos_template.tar.gz && \
    curl -sSL https://osf.io/download/dcf94/ -o OASIS-TRT_labels_to_OASIS_Atropos_template.tar.gz && \
    tar zxf OASIS-TRT_labels_to_OASIS_Atropos_template.tar.gz && \
    rm OASIS-TRT_labels_to_OASIS_Atropos_template.tar.gz' \
    --run-bash 'source /opt/miniconda-latest/etc/profile.d/conda.sh && \
    conda activate simple2 && \
    git clone https://github.com/nipy/mindboggle.git && \
    cd /opt/mindboggle && \
    git checkout edf95a3 && \
    python setup.py install && \
    sed -i "s/7.0/8.1/g" vtk_cpp_tools/CMakeLists.txt && \
    mkdir /opt/vtk_cpp_tools && \
    cd /opt/vtk_cpp_tools && \
    cmake /opt/mindboggle/vtk_cpp_tools && \
    make' \
    --env vtk_cpp_tools=/opt/vtk_cpp_tools > Dockerfile
    48 changes: 48 additions & 0 deletions label_T1.py
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    from glob import glob
    import os
    from nipype import Workflow, MapNode, Node
    from nipype.interfaces.ants import ApplyTransforms, AntsJointFusion, LabelGeometry
    from nipype.utils.misc import human_order_sorted

    T = glob('/data/out/ants_subjects/arno/antsTemplateToSubject*')[::-1]
    ref = '/data/T1.nii.gz'
    mask = '/data/out/ants_subjects/arno/antsBrainExtractionMask.nii.gz'

    T1s = human_order_sorted(glob('/opt/data/OASIS-TRT_brains_to_OASIS_Atropos_template/*.nii.gz'))
    labels = human_order_sorted(glob('/opt/data/OASIS-TRT_labels_to_OASIS_Atropos_template/*.nii.gz'))
    thickness = '/data/out/ants_subjects/arno/antsCorticalThickness.nii.gz'
    N = 20

    wf = Workflow('labelflow')
    transformer = MapNode(ApplyTransforms(), iterfield=['input_image'], name="transformer")
    transformer.inputs.reference_image = ref
    transformer.inputs.transforms = T
    transformer.inputs.input_image = T1s[:N]
    transformer.inputs.dimension = 3
    transformer.inputs.invert_transform_flags = [False, False]

    transformer_nn = MapNode(ApplyTransforms(), iterfield=['input_image'], name="transformer_nn")
    transformer_nn.inputs.reference_image = ref
    transformer_nn.inputs.transforms = T
    transformer_nn.inputs.dimension = 3
    transformer_nn.inputs.invert_transform_flags = [False, False]
    transformer_nn.inputs.input_image = labels[:N]
    transformer_nn.inputs.interpolation = 'NearestNeighbor'

    labeler = Node(AntsJointFusion(), name='labeler')
    labeler.inputs.dimension = 3
    labeler.inputs.target_image = [ref]
    labeler.inputs.out_label_fusion = 'label.nii.gz'
    labeler.inputs.mask_image = mask
    labeler.inputs.num_threads = 8
    wf.connect(transformer, 'output_image', labeler, 'atlas_image')
    wf.connect(transformer_nn, 'output_image', labeler, 'atlas_segmentation_image')

    tocsv = Node(LabelGeometry(), name='get_measures')
    tocsv.inputs.intensity_image = thickness
    wf.connect(labeler, 'out_label_fusion', tocsv, 'label_image')

    wf.base_dir = os.getcwd()

    wf.config['monitoring'] = {'enabled': True}
    wf.run('MultiProc')