Robust real time motion compensation for intraoperative video processing during neurosurgery
Résumé
A motion compensation method dedicated to intraoperative RGB video imaging in neurosurgery is presented in this work. The dedicated motion model proposed is based on subspace learning of the patient brain motion. The resolution method uses keypoints for a sparse, fast and robust estimation of the brain motion. Our results, obtained from in vivo data, show that our method is as accurate as standard motion estimation method while being much faster. It is also very robust to un-predicted events that can happen in the operative room and opens the way to intraoperative real time hemodynamics map during neurosurgery on human subjects.