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Maria Deprez focuses on image analysis and deep learning in the context of fetal, neonatal, and pediatric imaging. Her research includes motion-corrected MRI of the fetal brain and heart, spatio-temporal modeling, development, segmentation, and registration of atlases. She has contributed significantly to the field through various peer-reviewed conference papers and journal articles, addressing critical issues such as quantifying fetal periventricular white matter development using multimodal MRI and developing the Fetal Echocardiography Registration Network for effective 2D-to-3D alignment. Her work includes automated methods for craniofacial biometry and the application of deep learning techniques to enhance imaging quality and data interpretation. Deprez has also been involved in numerous collaborative research outputs, including studies on pulmonary development and multi-class fetal cardiac vessel segmentation. Her research is integral to advancing prenatal imaging methodologies and plays a crucial role in improving clinical applications in perinatal medicine.
King's College London • London, United Kingdom
Teaches and conducts research in medical imaging, focusing on advanced imaging techniques and applications in fetal and neonatal settings.
Requirements are consistent across King's Business School and Social Science & Public Policy departments for standard Master's entries.