Otology & Neurotology
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Hypothesis: To characterize anatomical measurements and shape variation of the facial nerve within the temporal bone, and to create statistical shape models (SSMs) to enhance knowledge of temporal bone anatomy and aid in automated segmentation.
Background: The facial nerve is a fundamental structure in otologic surgery, and detailed anatomic knowledge with surgical experience are needed to avoid its iatrogenic injury. Trainees can use simulators to practice surgical techniques, however manual segmentation required to develop simulations can be time consuming. Consequently, automated segmentation algorithms have been developed that use atlas registration, SSMs, and deep learning.
Methods: Forty cadaveric temporal bones were evaluated using three dimensional microCT (μCT) scans. The image sets were aligned using rigid fiducial registration, and the facial nerve canals were segmented and analyzed. Detailed measurements were performed along the various sections of the nerve. Shape variability was then studied using two SSMs: one involving principal component analysis (PCA) and a second using the Statismo framework.
Results: Measurements of the nerve canal revealed mean diameters and lengths of the labyrinthine, tympanic, and mastoid segments. The landmark PCA analysis demonstrated significant shape variation along one mode at the distal tympanic segment, and along three modes at the distal mastoid segment. The Statismo shape model was consistent with this analysis, emphasizing the variability at the mastoid segment. The models were made publicly available to aid in future research and foster collaborative work.
Conclusion: The facial nerve exhibited statistical variation within the temporal bone. The models used form a framework for automated facial nerve segmentation and simulation for trainees.
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Citation of this paper:
Hudson TJ, Gare B, Allen DG, Ladak HM, Agrawal SK. Intrinsic Measures and Shape Analysis of the Intratemporal Facial Nerve. Otol Neurotol. 2020 Mar;41(3):e378-e386. doi: 10.1097/MAO.0000000000002552. PMID: 31917770.