Macapype: An open multi-software framework for non-human primate anatomical MRI processing

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Poster No:


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Abstract Submission 


Bastien Cagna1, David Meunier2, Kep Kee Loh2, Régis Trapeau2, Julien Sein3, Sylvain Takerkart4, Olivier Coulon5, Pascal Belin6


1Institut des Neurosciences de la Timone, Aix-Marseille Université, Marseille, France, 2Institut des Neurosciences de la Timone, Aix-Marseille Université, Marseille, Bouches du Rhône (13), 3Aix-Marseille Université, Institut de Neurosciences de la Timone, Marseille, NA, 4CNRS - Aix Marseille Université, Marseille, France, 5Université Aix-Marseille/CNRS - Institut de Neurosciences de La Timone, Marseille, N/A, 6Aix-Marseille University, Marseille, PACA

First Author:

Bastien Cagna  
Institut des Neurosciences de la Timone, Aix-Marseille Université
Marseille, France


David Meunier  
Institut des Neurosciences de la Timone, Aix-Marseille Université
Marseille, Bouches du Rhône (13)
Kep Kee Loh  
Institut des Neurosciences de la Timone, Aix-Marseille Université
Marseille, Bouches du Rhône (13)
Régis Trapeau  
Institut des Neurosciences de la Timone, Aix-Marseille Université
Marseille, Bouches du Rhône (13)
Julien Sein  
Aix-Marseille Université, Institut de Neurosciences de la Timone
Marseille, NA
Sylvain Takerkart  
CNRS - Aix Marseille Université
Marseille, France
Olivier Coulon  
Université Aix-Marseille/CNRS - Institut de Neurosciences de La Timone
Marseille, N/A
Pascal Belin  
Aix-Marseille University
Marseille, PACA


Non-human primates (NHP) are increasingly used for cross-species neuroimaging studies, either for anatomical or functional comparison with human. Anatomical MR images are typically segmented in order to define regions of interest for fMRI and diffusion MRI analyses, work on surface reconstruction or localize implanted electrodes for electrophysiology. Although MRI processing is largely standardized in humans, it is still a challenge to define robust processing pipelines for segmentation of NHP anatomical images. Because acquisition parameters and experimental settings are much more variable in NHP than in human studies (size of animals, resolution, field of view, signal-to-noise ratio, availability of T2w images, etc.), there are multiple ways to perform each processing step (see for example Balbastre et al., 2017; Tasserie et al, 2019).
To unify processing of NHP anatomical MRI, we propose Macapype (, an open-source framework to create custom pipelines based on Nipype (Gorgolewski et al., 2011).


Nipype is a largely accepted Python framework for human MRI analysis, and provides "wraps" from different softwares, such as AFNI, FSL, SPM12, ANTS. Additionally, custom wraps specific to NHP preprocessing, such as AtlasBRex (Lohmeier et al, 2019) and NMT-based alignement (Seidlitz et al. 2018) are provided within the Macapype framework..
The Macapype package consists of several modules that may be configured depending on processing needs, such as: averaging multiple same acquisition; aligning T2-weighted image to T1-weighted image if available; denoising using non-local means methods; bias correction; skull stripping; segmentation between the different tissues (grey matter, white matter, cerebrospinal fluid).


Macapype provides a large modularity for all the steps described in the previous section. So far we have implemented two NHP MRI preprocessing pipelines:
- One takes T1w and T2w images to correct image bias. It aims at increasing the quality of the segmentation for surface reconstruction. The original script is written in bash, and make use of FSL, ANTS and atlasBREX, as well as the NMT template and tools (see Fig attached).
- The other provides an iterative sequence for normalization (template to source space) of T1w only, and provides segmentation with both SPM12 (OldSegment) and FSL FAST. The original script is written in Matlab, and make use of ANTS, FSL and SPM12.
A docker file is included in the package to allows users to get a fully embedded version of the pipelines working on any computer without previous installation of MRI processing software.
Both pipelines are compatible with BIDS (Gorgolewski et al. 2016) formatted input images.


Those two pipelines are setup as examples of NHP workflows in Macapype. Setting the original pipeline in an unified framework allows: 1) evaluation of the results obtained at the different pre-processing steps and 2) compatibility between the steps, in order to choose the most adapted steps for a given custom analysis (e.g. whether both T1w and T2w images are available , the quality of the acquisition due to antenna constraints, etc.).
Pipelines coming from other groups working on NHP MRI can be easily implemented within our framework, hence allowing comparative evaluation of the different solutions for a given acquisition setting. Using Nipype also allows to easily compare results with human brain segmentation, of particular interest for cross-species studies.

One of our most imminent goal is the preparation and diffusion of "ground truth" images, corresponding to the different steps defined earlier, allowing to evaluate the quality of pipelines using different softwares or different set of parameters.
To make this package easily usable by the whole community, we aim at validating it as a BIDSApp and providing an extensive documentation describing sub steps and examples.

Modeling and Analysis Methods:

Image Registration and Computational Anatomy
Segmentation and Parcellation 1

Neuroinformatics and Data Sharing:

Workflows 2

Novel Imaging Acquisition Methods:

Anatomical MRI


Other - Non-Human Primate

1|2Indicates the priority used for review
Supporting Image: Capturedu2019-12-1915-03-47.png

My abstract is being submitted as a Software Demonstration.


Please indicate below if your study was a "resting state" or "task-activation” study.


Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Not applicable

Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.


Please indicate which methods were used in your research:

Structural MRI

Which processing packages did you use for your study?

Free Surfer
Other, Please list  -   Nipype

Provide references using author date format

Balbastre Y et al. (2017). Primatologist: A modular segmentation pipeline for macaque brain morphometry. NeuroImage 162:306-321.
Gorgolewski, K et al. (2011). Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front. Neuroimform. 5:13.
Gorgolewski, K et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 3:160044.
Lohmeier, J et al. (2019). AtlasBREX: Automated template-derived brain extraction in animal MRI. Sci Rep. 9(1):12219
Seidlitz J et al. (2018). A population MRI brain template and analysis tools for the macaque. Neuroimage 170:121-131
Tasserie J et al. (2019). Pypreclin: An automatic pipeline for macaque functional MRI preprocessing. NeuroImage. doi:10.1016/j.neuroimage.2019.116353