Outcome—facial expressions
Facial expressions were recorded as 3D videos using non-invasive 4D stereophotogrammetry with nine cameras (6 greyscaled and 3 coloured) and 60 frames per seconds (see Fig. Motion capture system recording facial expressions as 3D videos using non-invasive 4D stereophotogrammetry with nine cameras (6 greyscaled and 3 coloured) from three directions (DI4D PRO System, Dimensional Imaging Ltd., Glasgow, UK). [All video captures and post-processing were performed by the same trained investigator (J.H.). The 3D videos were cut to analyse the movements from the last frame in rest position (= start frame) to the first frame with the facial expression at its maximum extent (= end frame) (DI4D Setup, Dimensional Imaging Ltd., Glasgow, UK). To track the facial movement, a dummy mesh was applied to the individual patient morphology in the first frame of each video (see Fig. Information captured by videostereophotogrammetry in the first frame. Left side: video data containing the colour and texture of the subject. Right side: dummy mesh applied to the individual patient morphology to track the facial movementSequence of exemplary 3D images recorded by the 4D system. The patient-specific mesh applied in the first frame was automatically tracked throughout the sequence of 3D images and manually corrected if necessaryTwo regions of interest were determined (see Fig. the lower face: the area of the lower face limited by a plane through the landmark subnasale parallel to Frankfort Horizontal containing the same automatically selected 559 points for each participant, andthe landmarks undergoing the biggest change during facial expression: cheilion left and right for maximum smile, and labrale superius and inferius for lip purse.Data analysis in Python. Left side: selection of the region of interest “lower face” limited by a plane through subnasale (green points). The region of interest was determined in the first patient analysed and automatically transferred to each subsequent patient. Right side: the landmarks undergoing the biggest change during facial expression (green points) were determined individually for each captureFor each facial expression, four parameters were calculated to objectively evaluate the movement (see Table Parameters calculated to objectively evaluate the facial expressionsMagnitude of facial expression in the most moving area of the lipsFor maximum smile: For lip purse: Differences in facial expression between the two most moving areas of the lipsFor maximum smile: For lip purse: d, Euclidian distanceExemplary illustration of the measurement “magnitude” for the facial expression maximum smile at the landmark cheilion right. The image at rest position (yellow) and the image at the maximum extent of the movement (transparent) are superimposed and the Euclidian distance between the landmark cheilion right at rest position and at maximum smile is measured (orange arrow)