This pipeline is developed by the Hagmann’s group at the University Hospital of Lausanne (CHUV) for use within the SNF Sinergia Project 170873 (), as well as for open-source software distribution.
Multi-Scale Brain Parcellator, part of the Connectome Mapping Toolkit (CMTK), is a BIDS App that implements a full anatomical MRI processing pipeline, from raw T1w data to structural brain parcellation at five different scales.
The Multi-Scale Brain Parcellator pipelines uses a combination of tools from well-known software packages, including FSL_, FreeSurfer_, ANTs_ as well as in-house tools from CMTK.
This tool allows you to easily do the following:
- Take T1 from raw to brain parcellations at 5 different scales.
- Implement tools from different software packages.
- Automate and parallelize processing steps, which provides a significant speed-up from typical linear, manual processing.
Reproducibility and replicability is achieved through the distribution of a BIDSApp, a software container image which provide a frozen environment where versions of all external softwares and libraries are fixed.
More information, installation instructions and documentation can be found at
This software is distributed under the open-source license Modified BSD. See license for more details.
If your are using the Multi-Scale Brain Parcellator in your work, please acknowledge this software and its dependencies. To help you to do so, we recommend you to use, modify to your needs, and include in your work the following text:
Results included in this manuscript come from the Multi-Scale Brain Parcellator version latest [1,2], a processing pipeline, written in Python which uses Nipype [3,4]. It is encapsulated in a BIDS app [5] based on Docker [6] and Singularity [7] container technologies. Resampling to isotropic resolution, Desikan-Killiany brain parcellation [8], brainstem parcellation [9], and hippocampal subfields segmentation [10] were performed using FreeSurfer 6.0.1. Final parcellations were created by performing cortical brain parcellation on at 5 different scales [11], probabilistic atlas-based segmentation of the thalamic nuclei [12],and combination of all segmented structures, using in-house CMTK tools and the antsRegistrationSyNQuick tool of ANTS v2.2.0 [13].
1.Tourbier S, Aleman-Gomez Y, Griffa A, Hagmann P (2019, October 22) sebastientourbier/multiscalebrainparcellator: Multi-Scale Brain Parcellator (Version v1.1.0). Zenodo. http://doi.org/10.5281/zenodo.2536778
2.Tourbier S, Aleman-Gomez Y, Griffa A, Bach Cuadra M, Hagmann P (2019, June 12). Multi-Scale Brain Parcellator: a BIDS App for the Lausanne Connectome Parcellation. 25th Annual Meeting of the Organization for Human Brain Mapping (OHBM), abstract #1714, poster #W616.
3.Gorgolewski K, Burns CD, Madison C, Clark D, Halchenko YO, Waskom ML, Ghosh SS (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front Neuroinform, vol. 5, no. 13. doi:10.3389/fninf.2011.00013.
4.Gorgolewski KJ, Esteban O, Ellis DG, Notter MP, Ziegler E, Johnson H, Hamalainen C, Yvernault B, Burns C, Manhães-Savio A, Jarecka D, Markiewicz CJ, Salo T, Clark D, Waskom M, Wong J, Modat M, Dewey BE, Clark MG, Dayan M, Loney F, Madison C, Gramfort A, Keshavan A, Berleant S, Pinsard B, Goncalves M, Clark D, Cipollini B, Varoquaux G, Wassermann D, Rokem A, Halchenko YO, Forbes J, Moloney B, Malone IB, Hanke M, Mordom D, Buchanan C, Pauli WM, Huntenburg JM, Horea C, Schwartz Y, Tungaraza R, Iqbal S, Kleesiek J, Sikka S, Frohlich C, Kent J, Perez-Guevara M, Watanabe A, Welch D, Cumba C, Ginsburg D, Eshaghi A, Kastman E, Bougacha S, Blair R, Acland B, Gillman A, Schaefer A, Nichols BN, Giavasis S, Erickson D, Correa C, Ghayoor A, Küttner R, Haselgrove C, Zhou D, Craddock RC, Haehn D, Lampe L, Millman J, Lai J, Renfro M, Liu S, Stadler J, Glatard T, Kahn AE, Kong X-Z, Triplett W, Park A, McDermottroe C, Hallquist M, Poldrack R, Perkins LN, Noel M, Gerhard S, Salvatore J, Mertz F, Broderick W, Inati S, Hinds O, Brett M, Durnez J, Tambini A, Rothmei S, Andberg SK, Cooper G, Marina A, Mattfeld A, Urchs S, Sharp P, Matsubara K, Geisler D, Cheung B, Floren A, Nickson T, Pannetier N, Weinstein A, Dubois M, Arias J, Tarbert C, Schlamp K, Jordan K, Liem F, Saase V, Harms R, Khanuja R, Podranski K, Flandin G, Papadopoulos Orfanos D, Schwabacher I, McNamee D, Falkiewicz M, Pellman J, Linkersdörfer J, Varada J, Pérez-García F, Davison A, Shachnev D, Ghosh S (2017). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. doi:10.5281/zenodo.581704.
5.Gorgolewski KJ, Alfaro-Almagro F, Auer T, Bellec P, Capota M, Chakravarty MM, Churchill NW, Cohen AL, Craddock RC, Devenyi GA, Eklund A, Esteban O, Flandin G, Ghosh SS, Guntupalli JS, Jenkinson M, Keshavan A, Kiar G, Liem F, Raamana PR, Raffelt D, Steele CJ, Quirion P, Smith RE, Strother SC, Varoquaux G, Wang Y, Yarkoni T, Poldrack RA (2017). BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLOS Computational Biology, vol.13, no. 3, e1005209. doi:10.1371/journal.pcbi.1005209.
6.Merkel D (2014). Docker: lightweight Linux containers for consistent development and deployment. Linux Journal, vol. 2014, no. 239. https://dl.acm.org/citation.cfm?id=2600239.2600241
7.Kurtzer GM, Sochat V, Bauer MW (2017). Singularity: Scientific containers for mobility of compute. PLoS ONE, vol. 12, no. 5, e0177459. doi: 10.1371/journal.pone.0177459
8.Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, vol. 31, no. 3, pp. 968-980. doi:10.1016/j.neuroimage.2006.01.021.
9.Iglesias JE, Van Leemput K, Bhatt P, Casillas C, Dutt S, Schuff N, Truran-Sacrey D, Boxer A, Fischl B (2015). Bayesian segmentation of brainstem structures in MRI. Neuroimage, vol. 113, pp. 184-195. doi: 10.1016/j.neuroimage.2015.02.065.
10.Iglesias JE, Augustinack JC, Nguyen K, Player CM, Player A, Wright M, Roy N, Frosch MP, McKee AC, Wald LL, Fischl B, Van Leemput K (2015). A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI. Neuroimage, vol. 115, July, pp. 117-137. doi: 10.1016/j.neuroimage.2015.04.042.
11.Cammoun L, Gigandet X, Meskaldji D, Thiran JP, Sporns O, Do KQ, Maeder P, Meuli RA, Hagmann P (2012). Mapping the human connectome at multiple scales with diffusion spectrum MRI. Journal of neuroscience methods, vol. 203, no. 2, pp. 386-397. doi: 10.1016/j.jneumeth.2011.09.031.
12.Najdenovska E, Alemán-Gómez Y, Battistella G, Descoteaux M, Hagmann P, Jacquemont S, Maeder P, Thiran JP, Fornari E, Bach Cuadra M (2018). In-vivo probabilistic atlas of human thalamic nuclei based on diffusion- weighted magnetic resonance imaging. Scientific Data, vol. 5, no. 180270. doi: 10.1038/sdata.2018.270
13.Avants BB, Epstein CL, Grossman M, Gee JC (2008). Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, vol. 12, no. 1, pp. 26–41. doi:10.1016/j.media.2007.06.004.
This App has the following command line arguments:
$ docker -ti --rm sebastientourbier/multiscalebrainparcellator --help
usage: multiscalebrainparcellator_bidsapp_entrypointscript [-h]
[--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
[--thalamic_nuclei]
[--hippocampal_subfields]
[--brainstem_structures]
[--fs_license]
[-v]
bids_dir output_dir {participant}
Multi-scale Brain Parcellator BIDS App entrypoint script.
positional arguments:
bids_dir The directory with the input dataset formatted
according to the BIDS standard.
output_dir The directory where the output files should be
stored.
If you are running group level analysis this folder should be pre-populated with the results of the participant level analysis.
{participant} Level of the analysis that will be performed. Note
that only the participant level analysis is available. Multiple participant level analyses can be run independently (in parallel) using the same output_dir.
optional arguments:
-h, --help show this help message and exit
--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]
The label(s) of the participant(s) that should be analyzed. The label corresponds to
sub-<participant_label> from the BIDS spec (so it does not include "sub-"). If this parameter is not provided all subjects should be analyzed. Multiple participants can be specified with a space separated list.
--isotropic_resolution RESOLUTION_IN_MM
Custom isotropic resolution used for resampling
the structural image at the beginning of the processing pipeline. If not set, an isotropic resolution of 1mm is used.
--thalamic_nuclei Segment thalamic thalamic_nuclei
--hippocampal_subfields Segment hippocampal subfields (FreeSurfer)
--brainstem_structures Segment brainstem structures (FreeSurfer)
--fs_license Specify the Freesurfer license location inside the container image
-v, --version show program's version number and exit
To run it in participant level mode (for one participant):
$ docker run -it --rm \
-v /home/localadmin/data/ds001:/bids_dir \
-v /home/localadmin/data/ds001/derivatives:/output_dir \
-v /usr/local/freesurfer/license.txt:/bids_dir/code/license.txt \
sebastientourbier/multiscalebrainparcellator:latest \
/bids_dir /output_dir participant --participant_label 01 \
--fs_license /bids_dir/code/license.txt \
--thalamic_nuclei \
--hippocampal_subfields \
--brainstem_structures \
--isotropic_resolution 1.0
- Sebastien Tourbier (sebastientourbier)
- Yasser Aleman (yasseraleman)
- Alessandra Griffa (agriffa)
- Patric Hagmann (pahagman)
Work supported by the Sinergia SNF-170873 Grant.
Copyright (C) 2017-2019, Brain Communication Pathways Sinergia Consortium and the Multi Scale Brain Parcellator developers, Switzerland.