Primatologist: A modular segmentation pipeline for macaque brain morphometry

&NA; Because they bridge the genetic gap between rodents and humans, non‐human primates (NHPs) play a major role in therapy development and evaluation for neurological disorders. However, translational research success from NHPs to patients requires an accurate phenotyping of the models. In patients, magnetic resonance imaging (MRI) combined with automated segmentation methods has offered the unique opportunity to assess in vivo brain morphological changes. Meanwhile, specific challenges caused by brain size and high field contrasts make existing algorithms hard to use routinely in NHPs. To tackle this issue, we propose a complete pipeline, Primatologist, for multi‐region segmentation. Tissue segmentation is based on a modular statistical model that includes random field regularization, bias correction and denoising and is optimized by expectation‐maximization. To deal with the broad variety of structures with different relaxing times at 7 T, images are segmented into 17 anatomical classes, including subcortical regions. Pre‐processing steps insure a good initialization of the parameters and thus the robustness of the pipeline. It is validated on 10 T2‐weighted MRIs of healthy macaque brains. Classification scores are compared with those of a non‐linear atlas registration, and the impact of each module on classification scores is thoroughly evaluated. HighlightsA segmentation pipeline is proposed to the non‐human primate neuroimaging community.It allows automatic segmentation of Macaque brain MRIs into 17 anatomical classes.It relies on a generative model of intensity and a 3D digital atlas.We showed that Primatologist performs better than a conventional atlas registration.