Primatologist: A modular segmentation pipeline for macaque brain morphometry.

Area of Science:

• Neuroscience
• Medical Imaging
• Computational Biology

Background:

• Non-human primates (NHPs) are vital for neurological disorder research due to genetic similarities with humans.
• Accurate phenotyping of NHP models is essential for successful translation of therapies from preclinical studies to human patients.
• Magnetic resonance imaging (MRI) and automated segmentation are powerful tools for in vivo brain analysis, but current methods face challenges with NHP brain size and high-field MRI contrasts.

Purpose of the Study:

• To develop and validate a robust, automated pipeline named Primatologist for multi-region brain segmentation in non-human primates.
• To address the technical challenges of applying existing segmentation algorithms to NHP neuroimaging data, particularly at 7 Tesla (7T).
• To enable more accurate phenotyping of NHP models for improved translational research in neurological disorders.

Main Methods:

• Developed a complete segmentation pipeline, Primatologist, employing a modular statistical model with random field regularization, bias correction, and denoising.
• Optimized the segmentation model using the expectation-maximization algorithm.
• Segmented images into 17 distinct anatomical classes, including subcortical regions, to accommodate diverse tissue contrasts at 7T.
• Incorporated pre-processing steps to ensure robust parameter initialization and pipeline performance.

Main Results:

• The Primatologist pipeline demonstrated effective multi-region segmentation of NHP brains.
• The pipeline successfully segmented 17 anatomical classes, handling variations in relaxation times at 7T.
• Validation on T2-weighted MRIs of macaque brains showed comparable or improved classification scores against non-linear atlas registration.
• Individual module contributions to classification accuracy were thoroughly evaluated.

Conclusions:

• The Primatologist pipeline provides a robust and accurate solution for automated brain segmentation in non-human primates.
• This advancement facilitates precise phenotyping of NHP models, significantly enhancing their utility in neurological disorder research.
• The developed tool is expected to accelerate the development and evaluation of therapies bridging the gap between NHP models and human patients.