Statistical Shape Model of the Temporal Bone Using Segmentation Propagation.

Hypothesis: Automated image registration techniques can successfully determine anatomical variation in human temporal bones with statistical shape modeling.

Background: There is a lack of knowledge about inter-patient anatomical variation in the temporal bone. Statistical shape models (SSMs) provide a powerful method for quantifying variation of anatomical structures in medical images but are time-intensive to manually develop. This study presents SSMs of temporal bone anatomy using automated image-registration techniques.

Methods: Fifty-three cone-beam temporal bone CTs were included for SSM generation. The malleus, incus, stapes, bony labyrinth, and facial nerve were automatically segmented using 3D Slicer and a template-based segmentation propagation technique. Segmentations were then used to construct SSMs using MATLAB. The first three principal components of each SSM were analyzed to describe shape variation.

Results: Principal component analysis of middle and inner ear structures revealed novel modes of anatomical variation. The first three principal components for the malleus represented variability in manubrium length (mean: 4.47 mm; ±2-SDs: 4.03-5.03 mm) and rotation about its long axis (±2-SDs: -1.6° to 1.8° posteriorly). The facial nerve exhibits variability in first and second genu angles. The bony labyrinth varies in the angle between the posterior and superior canals (mean: 88.9°; ±2-SDs: 83.7°-95.7°) and cochlear orientation (±2-SDs: -4.0° to 3.0° anterolaterally).

Conclusions: SSMs of temporal bone anatomy can inform surgeons on clinically relevant inter-patient variability. Anatomical variation elucidated by these models can provide novel insight into function and pathophysiology. These models also allow further investigation of anatomical variation based on age, BMI, sex, and geographical location.