iBEAT: A toolbox for infant brain magnetic resonance image processing

Area of Science:

• Neuroimaging
• Medical Image Analysis
• Developmental Neuroscience

Background:

• Infant brain magnetic resonance (MR) image analysis presents significant challenges due to small brain size and low tissue contrast.
• Accurate analysis is crucial for understanding early brain development and identifying potential abnormalities.

Purpose of the Study:

• To introduce the Infant Brain Extraction and Analysis Toolbox (iBEAT), a comprehensive software package for processing infant brain MR images.
• To integrate state-of-the-art algorithms for robust brain extraction, tissue segmentation, and region labeling in infant neuroimaging.

Main Methods:

• iBEAT employs a learning-based meta-algorithm for brain extraction, combining results from existing algorithms (BET, BSE).
• Tissue segmentation is achieved using a level-sets-based algorithm incorporating multimodality, cortical thickness, and longitudinal consistency constraints.
• Region of Interest (ROI) labeling is performed using the HAMMER (Hierarchical Attribute Matching Mechanism for Elastic Registration) algorithm.

Main Results:

• The iBEAT toolbox accurately segments and labels infant brain MR images.
• The software effectively processes both single-time-point (cross-sectional) and multiple-time-point (longitudinal) infant brain images.
• Comprehensive performance evaluation on hundreds of infant brain images validated the toolbox's capabilities.

Conclusions:

• iBEAT provides an accurate and integrated solution for infant brain MR image analysis.
• The toolbox supports both cross-sectional and longitudinal studies, facilitating a wide range of neurodevelopmental research.
• iBEAT is freely available as a Linux-based standalone package, promoting accessibility in the research community.