Deep Learning for Analysis of Stroke in Infants
We are looking for a master student with a strong interest in medical image analysis. The topic of this project focuses mainly on automatic stroke detection and segmentation in brain MRI of neonates. We have a close collaboration with the neonatology department and access to the brain MRI of neonates with stroke. Infants with stroke are scanned immediately after diagnosis. About three months later another MRI is made. The main aim of this study is to quantify the brain development of these infants affected by stroke using MR images made at these two time points.
We are looking for an applicant with knowledge of machine learning algorithms (or deep learning) and programming skills. A basic background on image analysis or medical imaging would be an advantage. The project can start as soon as possible. If you are interested in this project, please contact Nadieh Khalili or dr. Ivana Išgum.
Functional MRI of Premature Infants
Functional MRI is an increasingly used tool in research and clinical settings. Besides for adults, the technique is used nowadays also for mapping brain functions in children. Several reports even indicate that fMRI is feasible for infants, including neonates and prematurely born babies. In most cases, fMRI in these groups will involve passive tasks during natural sleep or sedation. Because of the size of the infant’s brain, standard tools for, for example, brain segmentation into ROIs (to be able to compute fMRI activation in each of these ROIs) and normalization (necessary for averaging over subjects) are not adequate and analysis of infant fMRI data will require dedicated tools.
We are looking for an MSc student in (biomedical) engineering, artificial intelligence, computer science, or comparable training, with an interest in medical image analysis and deep learning to design tools for this analysis. If you are interested in this project, please contact dr. Ivana Išgum.