Fetal Brain

Fetal brain research focuses on understanding brain development in utero, primarily using advanced imaging techniques like MRI and ultrasound. Current research heavily employs deep learning models, including U-Nets and transformers, to automate tasks such as brain segmentation, tractography, and anomaly detection, often incorporating techniques like multi-task learning, domain adaptation, and data augmentation to address challenges posed by limited data and image quality. These advancements are significantly improving the accuracy and efficiency of fetal brain analysis, leading to better understanding of normal and abnormal development and potentially improving prenatal diagnosis and care.