An automatic, objective method to measure and visualize volumetric changes in patients with facial palsy during 3D video recordings

Introduction Using grading systems, the severity of facial palsy is typically classified through static 2D images. These approaches fail to capture crucial facial attributes, such as the depth of the nasolabial fold. We present a novel technique that uses 3D video recordings to overcome this limitation. Our method automatically characterizes the facial structure, calculates volumetric disparities between the affected and contralateral side, and includes an intuitive visualization.