Intrinsic Measures and Shape Analysis of the Intratemporal Facial Nerve

Abstract

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.

Fully automated preoperative segmentation of temporal bone structures from clinical CT scans

“…micro-CT scans and manually placed fiducials to identify facial nerves in cadavers 13 . However, these methods require significant manual input, and therefore may be limited in their scalability and subject to user-dependent variability.…”

Micro‐CT of the human ossicular chain: Statistical shape modeling and implications for otologic surgery

“…Statistical shape modeling is a technique for morphological study which uses a database of 3D model samples to calculate an average geometry and analyze how this geometry varies across samples (Cootes, Taylor, Cooper, & Graham, 1995). Statistical shape modeling has been used in the study of many anatomical entities, for example, the nasal cavity (Keustermans et al, 2018), the sigmoid sinus (Van Osch et al, 2019), and the facial nerve (Hudson, Gare, Allen, Ladak, & Agrawal, 2020). Not only are statistical shape models (SSMs) useful for direct study of structure but they also serve as a basis for creating automated segmentation algorithms, the development of which would replace exhaustive manual image segmentation (subdividing the region of interest from surrounding anatomy).…”