Mindboggle’s open source brain morphometry platform takes in preprocessed T1-weighted MRI data, and outputs volume, surface, and tabular data containing label, feature, and shape information for further analysis. Mindboggle can be run on the command line as “mindboggle” and also exists as a cross-platform Docker container for convenience and reproducibility of results. The software runs on Linux and is written in Python 3 and Python-wrapped C++ code called within a Nipype pipeline framework. We have tested the software most extensively with Python 3.5.1 on Ubuntu Linux 14.04.

Date:September 19, 2017



A Klein, SS Ghosh, FS Bao, J Giard, Y Hame, E Stavsky, N Lee, B Rossa, M Reuter, EC Neto, A Keshavan. 2017. Mindboggling morphometry of human brains. PLoS Computational Biology 13(3): e1005350. doi:10.1371/journal.pcbi.1005350

Getting help

If you have any questions about Mindboggle, please post to NeuroStars with the tag “mindboggle”. If you have found a bug, big or small, please submit an issue on GitHub.

To run the mindboggle jupyter notebook tutorial, first install the mindboggle docker container (see below) and enter the bash shell of the container from your $HOST (e.g., /Users/arno):

docker run --rm -ti -v $HOST:/home/jovyan/work -p 8888:8888 --entrypoint /bin/bash nipy/mindboggle

Then run the notebook from within the container:

jupyter notebook /opt/mindboggle/docs/mindboggle_tutorial.ipynb


We recommend installing Mindboggle and its dependencies as a cross-platform Docker container for greater convenience and reproducibility of results. All the examples below assume you are using this Docker container, with the path /home/jovyan/work/ pointing to your host machine. (Alternatively, one can create a Singularity image, or Mindboggle can be installed from scratch on a Linux machine using this script).

1. Install and run Docker on your (macOS, Linux, or Windows) host machine.

2. Download the Mindboggle Docker container (copy/paste the following in a terminal window):

docker pull nipy/mindboggle

Note 1: This contains FreeSurfer, ANTs, and Mindboggle, so it is currently over 6GB.

Note 2: You may need to increase memory allocated by Docker to at least 5GB. For example: By default, Docker for `Mac is set to use 2 GB runtime memory <>`_.

3. Optionally download sample data. To try out the mindboggle examples below, download and unzip the directory of example input data (455 MB). For example MRI data to preprocess with FreeSurfer and ANTs software, download and unzip (29 MB).

4. Optionally set environment variables for clarity in the commands below (modify accordingly, except for DOCK):

HOST=/Users/binarybottle  # path on host to access input/output
DOCK=/home/jovyan/work  # path to HOST from Docker container
IMAGE=$DOCK/example_mri_data/T1.nii.gz  # input image on HOST
ID=arno  # ID for brain image

Run one command

The Mindboggle Docker container can be run as a single command to process a T1-weighted MR brain image through FreeSurfer, ANTs, and Mindboggle. Skip to the next section if you wish to run recon-all,, and mindboggle differently:

docker run --rm -ti -v $HOST:$DOCK nipy/mindboggle $IMAGE --id $ID

Outputs are stored in $DOCK/mindboggle123_output/ by default, but you can set a different output path with --out $OUT.

Run separate commands

If finer control is needed over the software in the Docker container, the following instructions outline how to run each command separately. Mindboggle currently takes output from FreeSurfer and optionally from ANTs. FreeSurfer version 6 or higher is recommended because by default it uses Mindboggle’s DKT-100 surface-based atlas to generate corresponding labels on the cortical surfaces and in the cortical and non-cortical volumes (v5.3 generates these surface labels by default; older versions require “-gcs DKTatlas40.gcs” to generate these surface labels).

1. Enter the Docker container’s bash shell to run recon-all,, and mindboggle commands:

docker run --rm -ti -v $HOST:$DOCK --entrypoint /bin/bash nipy/mindboggle

2. FreeSurfer generates labeled cortical surfaces, and labeled cortical and noncortical volumes. Run recon-all on a T1-weighted IMAGE file (and optionally a T2-weighted image), and set the output ID name as well as the $FREESURFER_OUT output directory:


recon-all -all -i $IMAGE -s $ID -sd $FREESURFER_OUT

3. ANTs provides brain volume extraction, segmentation, and registration-based labeling. generates transforms and segmentation files used by Mindboggle, and is run on the same IMAGE file and ID as above, with $ANTS_OUT output directory. TEMPLATE points to the OASIS-30_Atropos_template folder already installed in the Docker container (“\” splits the command for readability):

TEMPLATE=/opt/data/OASIS-30_Atropos_template -d 3 -a $IMAGE -o $ANTS_OUT/$ID/ants \
  -e $TEMPLATE/T_template0.nii.gz \
  -t $TEMPLATE/T_template0_BrainCerebellum.nii.gz \
  -m $TEMPLATE/T_template0_BrainCerebellumProbabilityMask.nii.gz \
  -f $TEMPLATE/T_template0_BrainCerebellumExtractionMask.nii.gz \
  -p $TEMPLATE/Priors2/priors%d.nii.gz

4. Mindboggle can be run on data preprocessed by recon-all and as above by setting:

OUT=$DOCK/mindboggled  # output folder

Or it can be run on the mindboggle_input_example preprocessed data by setting:

OUT=$DOCK/mindboggled  # output folder

Example Mindboggle commands:

To learn about Mindboggle’s command options, type this in a terminal window:

mindboggle -h

Example 1: Run Mindboggle on data processed by FreeSurfer but not ANTs:

mindboggle $FREESURFER_SUBJECT --out $OUT

Example 2: Same as Example 1 with output to visualize surface data with roygbiv:

mindboggle $FREESURFER_SUBJECT --out $OUT --roygbiv

Example 3: Take advantage of ANTs output as well (“\” splits for readability):

mindboggle $FREESURFER_SUBJECT --out $OUT --roygbiv \
    --ants $ANTS_SUBJECT/antsBrainSegmentation.nii.gz

Example 4: Generate only volume (no surface) labels and shapes:

mindboggle $FREESURFER_SUBJECT --out $OUT \
    --ants $ANTS_SUBJECT/antsBrainSegmentation.nii.gz \

Visualize output

To visualize Mindboggle output with roygbiv, start the Docker image with:

docker run --rm -ti -v $HOST:$DOCK -p 5000:5000 --entrypoint /bin/bash nipy/mindboggle

and then inside the image, run roygbiv on an output directory:

roygbiv $OUT/$ID

and open a browser to localhost:5000.

Right now, roygbiv only shows summarized data, but Anisha Keshavan is working on by-vertex visualizations (for the latter, try Paraview).

Appendix: processing

The following steps are performed by Mindboggle (with links to code on GitHub):

  1. Create hybrid gray/white segmentation from FreeSurfer and ANTs output (combine_2labels_in_2volumes).

  2. Fill hybrid segmentation with FreeSurfer- or ANTs-registered labels.

  3. Compute volume shape measures for each labeled region:

  4. Compute surface shape measures for every cortical mesh vertex:

  5. Extract cortical surface features:

  6. For each cortical surface label/sulcus, compute:

  7. Compute statistics (stats_per_label in for each shape measure in #4 for each label/feature:

    • median
    • median absolute deviation
    • mean
    • standard deviation
    • skew
    • kurtosis
    • lower quartile
    • upper quartile

Appendix: output

Example output data can be found on Mindboggle’s examples site on By default, output files are saved in $HOME/mindboggled/SUBJECT, where $HOME is the home directory and SUBJECT is a name representing the person’s brain that has been scanned. Volume files are in NIfTI format, surface meshes in VTK format, and tables are comma-delimited. Each file contains integers that correspond to anatomical labels or features (0-24 for sulci). All output data are in the original subject’s space. The following include outputs from most, but not all, optional arguments.

Folder Contents Format
labels/ number-labeled surfaces and volumes .vtk, .nii.gz
features/ surfaces with features: sulci, fundi .vtk
shapes/ surfaces with shape measures (per vertex) .vtk
tables/ tables of shape measures (per label/feature/vertex) .csv

mindboggled / $SUBJECT /

labels /

freesurfer_wmparc_labels_in_hybrid_graywhite.nii.gz: hybrid segmentation filled with FS labels

ants_labels_in_hybrid_graywhite.nii.gz: hybrid segmentation filled with ANTs + FS cerebellar labels

[left,right]_cortical_surface / freesurfer_cortex_labels.vtk: DKT cortical surface labels

features / [left,right]_cortical_surface /

folds.vtk: (unidentified) depth-based folds

sulci.vtk: sulci defined by DKT label pairs in depth-based folds

fundus_per_sulcus.vtk: fundus curve per sulcus – UNDER EVALUATION –

cortex_in_MNI152_space.vtk: cortical surfaces aligned to an MNI152 template

shapes / [left,right]_cortical_surface /

area.vtk: per-vertex surface area

mean_curvature.vtk: per-vertex mean curvature

geodesic_depth.vtk: per-vertex geodesic depth

travel_depth.vtk: per-vertex travel depth

freesurfer_curvature.vtk: FS curvature files converted to VTK

freesurfer_sulc.vtk: FS sulc (convexity) files converted to VTK

freesurfer_thickness.vtk: FS thickness files converted to VTK

tables /

volume_per_freesurfer_label.csv: volume per FS label

volumes_per_ants_label.csv: volume per ANTs label

thickinthehead_per_freesurfer_cortex_label.csv: FS cortex label thickness

thickinthehead_per_ants_cortex_label.csv: ANTs cortex label thickness

[left,right]_cortical_surface /

label_shapes.csv: per-label surface shape statistics

sulcus_shapes.csv: per-sulcus surface shape statistics

fundus_shapes.csv: per-fundus surface shape statistics – UNDER EVALUATION –

vertices.csv: per-vertex surface shape statistics