Intra-temporal facial nerve centerline segmentation for navigated temporal bone surgery

Approaches through the temporal bone require surgeons to drill away bone to expose a target skull base lesion while evading vital structures contained within it, such as the sigmoid sinus, jugular bulb, and facial nerve. We hypothesize that an augmented neuronavigation system that continuously calculates the distance to these structures and warns if the surgeon drills too close, will aid in making safe surgical approaches. Contemporary image guidance systems are lacking an automated method to segment the inhomogeneous and complexly curved facial nerve. Therefore, we developed a segmentation method to delineate the intra-temporal facial nerve centerline from clinically available temporal bone CT images semi-automatically. Our method requires the user to provide the start- and end-point of the facial nerve in a patient's CT scan, after which it iteratively matches an active appearance model based on the shape and texture of forty facial nerves. Its performance was evaluated on 20 patients by comparison to our gold standard: manually segmented facial nerve centerlines. Our segmentation method delineates facial nerve centerlines with a maximum error along its whole trajectory of 0.40±0.20 mm (mean±standard deviation). These results demonstrate that our model-based segmentation method can robustly segment facial nerve centerlines. Next, we can investigate whether integration of this automated facial nerve delineation with a distance calculating neuronavigation interface results in a system that can adequately warn surgeons during temporal bone drilling, and effectively diminishes risks of iatrogenic facial nerve palsy. © 2011 SPIE

Intra-temporal facial nerve centerline segmentation for navigated temporal bone surgery

Approaches through the temporal bone require surgeons to drill away bone to expose a target skull base lesion while evading vital structures contained within it, such as the sigmoid sinus, jugular bulb, and facial nerve. We hypothesize that an augmented neuronavigation system that continuously calculates the distance to these structures and warns if the surgeon drills too close, will aid in making safe surgical approaches. Contemporary image guidance systems are lacking an automated method to segment the inhomogeneous and complexly curved facial nerve. Therefore, we developed a segmentation method to delineate the intra-temporal facial nerve centerline from clinically available temporal bone CT images semi-automatically. Our method requires the user to provide the start- and end-point of the facial nerve in a patient's CT scan, after which it iteratively matches an active appearance model based on the shape and texture of forty facial nerves. Its performance was evaluated on 20 patients by comparison to our gold standard: manually segmented facial nerve centerlines. Our segmentation method delineates facial nerve centerlines with a maximum error along its whole trajectory of 0.40±0.20 mm (mean±standard deviation). These results demonstrate that our model-based segmentation method can robustly segment facial nerve centerlines. Next, we can investigate whether integration of this automated facial nerve delineation with a distance calculating neuronavigation interface results in a system that can adequately warn surgeons during temporal bone drilling, and effectively diminishes risks of iatrogenic facial nerve palsy. © 2011 SPIE