A FreeSurfer-compliant consistent manual segmentation of infant brains spanning the 0-2 year age range

Area of Science:

• Neuroimaging
• Developmental Neuroscience
• Medical Image Analysis

Background:

• Accurate brain segmentation is crucial for understanding neurodevelopment.
• Existing segmentation tools may not be optimized for the unique characteristics of infant brains.
• A standardized approach is needed for analyzing pediatric neuroimaging data.

Purpose of the Study:

• To develop and validate FreeSurfer compatible segmentation guidelines specifically for infant MRI scans.
• To create a unique dataset of manually segmented infant brain MRI acquisitions.
• To facilitate research in pediatric neuroscience and medicine.

Main Methods:

• Detailed description of novel segmentation guidelines for infant brain MRI.
• Manual segmentation of a diverse dataset of infant brain MRI scans.
• Ensuring FreeSurfer compatibility for broad usability.

Main Results:

• A comprehensive set of segmentation guidelines tailored for infant neuroimaging.
• A unique, manually curated dataset of infant brain MRI scans (ages 0-2 years).
• The dataset features a near-even age distribution, enhancing its utility.

Conclusions:

• The developed guidelines and dataset provide a valuable resource for the neuroscience and medical research communities.
• These tools will aid in the detailed study of early brain development and related conditions.
• Potential applications span various fields within pediatric medicine and brain research.