Deep infant brain segmentation from multi-contrast MRI.

Area of Science:

• Neuroimaging
• Medical Image Analysis
• Pediatric Radiology

Background:

• Accurate segmentation of pediatric brain MRI is crucial for studying brain development.
• Challenges include developmental changes, imaging constraints, motion artifacts, and diverse protocols.
• Existing segmentation models are often specialized and lack robustness for clinical variability.

Purpose of the Study:

• To develop a versatile deep learning framework for robust brain MRI segmentation in infants and young children.
• To address the fragmentation of existing specialized segmentation methods.
• To improve segmentation accuracy and efficiency across diverse pediatric neuroimaging data.

Main Methods:

• Developed BabySeg, a deep learning framework utilizing domain randomization to synthesize varied training images.
• Implemented a feature pooling mechanism allowing models to integrate information from multiple input scans.
• Trained and validated the framework on diverse pediatric MRI datasets with varying protocols and age groups.

Main Results:

• BabySeg achieved state-of-the-art performance in segmenting pediatric brain MRIs.
• The single model demonstrated comparable or superior accuracy to existing methods across different age cohorts and input configurations.
• The framework processed images significantly faster than many existing tools.

Conclusions:

• BabySeg offers a unified and robust solution for pediatric brain MRI segmentation, overcoming limitations of specialized models.
• The framework's adaptability to diverse imaging protocols and its high accuracy make it suitable for clinical and research applications.
• This approach advances the analysis of early human brain development through improved neuroimaging segmentation.