MacaSurfer: automated surface-volume mapping across the macaque lifespan

Macaque MRI is central to translational, developmental, and comparative neuroscience. However, automated structural processing of macaque MRI remains challenging because image quality varies substantially across sites, standardized non-human primate imaging protocols are lacking, and developmental changes alter image contrast throughout postnatal development. Existing approaches, mostly adapted from human neuroimaging or designed for isolated processing steps, fail to generate robust and anatomically consistent surface-volume representations across heterogeneous datasets and developmental stages. Here we introduce MacaSurfer, a fully automated, containerized framework for unified surface-volume mapping across the macaque lifespan. MacaSurfer combines deep-learning tissue segmentation model, tissue-guided bias-field correction, topology-aware surface reconstruction, and surface-aware volumetric registration to jointly optimize cortical geometry and volumetric anatomy mapping for macaque MRI. Validated on 1,346 imaging sessions from 965 macaques across 39 international sites and spanning 2 weeks to 23 years of age, MacaSurfer demonstrated anatomical consistency across diverse acquisition conditions, high test-retest precision, and robustness to controlled image degradation. Leveraging MacaSurfer-derived morphometry, we established normative trajectories from 835 macaques, providing a standardized reference for downstream individualized deviation analysis. Together with openly available source code, containers, and pretrained models, MacaSurfer provides a standardized and reproducible ecosystem to accelerate large-scale developmental, translational, and comparative neuroimaging in nonhuman primates.