The Medical Image Analysis group develops advanced medical image analysis technologies, and related translational biomedical engineering technologies, to quantify, diagnose, and follow-up disorders related to the central nervous system (e.g. glioblastomas, stroke, multiple sclerosis, etc.).
The group develops novel techniques for multimodal image segmentation and analysis of brain lesions, presently including glioblastoma multiforme, multiple sclerosis, and acute ischemic stroke. The results of these developments are aimed at advancing the fields of radiomics for the discovery of innovative noninvasive imaging biomarkers used to characterize disease and guide the decision-making process, as well as in radio-therapy, neuro-surgery, drug-development, etc. The developments revolve around the vision of scalable, adaptable, and time-effective machine-learning algorithms developed with a strong focus on clinical applicability. The group further supports these developments with dedicated techniques for super-resolution imaging, aiming at bridging information from low and high levels of image resolution, and fast and robust human-machine interfacing, designed to leverage the communication between computer algorithms and expert domain knowledge.