Infant Brain

Research on the infant brain focuses on developing accurate and efficient methods for analyzing brain images, primarily using MRI, to understand brain development and detect neurological conditions. Current efforts utilize advanced deep learning architectures, including U-Nets, generative adversarial networks (GANs), and diffusion models, often incorporating attention mechanisms and domain adaptation techniques to address challenges posed by low contrast, incomplete data, and variations in image acquisition. These advancements improve the accuracy of brain tissue segmentation, cortical surface reconstruction, and the detection of lesions, ultimately aiding in early diagnosis and monitoring of neurodevelopmental disorders. The resulting improvements in image analysis tools have significant implications for both clinical practice and neuroscience research.