Exploration of Automated Measurement for Ossicular Chains Based on 3-Dimensional Geometric Information

Abnormalities in the ossicular chain, a key middle-ear component that is crucial for sound transmission, can lead to conductive hearing loss; reconstruction offers an effective treatment. Accurate preoperative ossicular-chain measurements are essential for creating prostheses; however, current methods rely on cadaver studies or manual measurements from 2-dimensional images, which are time-intensive and laborious and depend heavily on radiologist expertise. To improve efficiency, we aimed to develop a systematic approach for automated ossicular-chain segmentation and measurement using ultra-high-resolution computed tomography (U-HRCT). One hundred forty patients (226 ears) with normal ear anatomy underwent U-HRCT. Twelve parameters were defined to measure ossicular-chain components. Automated measurements based on automated segmentation of 226 ear images were verified through manual measurements. We analyzed variations by ear side, sex, and age group. Stapes analysis was limited by segmentation accuracy. Complete segmentation of the malleus, incus, and stapes was achieved in 47 ears. Automated measurements of 8 parameters showed no significant differences compared to manual measurements in 47 cases. Significant sex-based differences emerged in all parameters except stapes footplate length, incudostapedial joint angle, and stapes volume (<i>P</i> = 0.205, <i>P</i> = 0.560, and <i>P</i> = 0.170, respectively). Notable side-specific differences were observed in female incus height and male malleus volume (<i>P</i> = 0.017 and <i>P</i> = 0.037, respectively). No statistically significant differences were found in other parameters across different age groups, except for malleus and incus volumes (<i>P</i> = 0.015 and <i>P</i> = 0.031). The proposed algorithm effectively automated ossicular-chain segmentation and measurement, establishing a normative range for ossicular parameters and providing a valuable reference for detecting abnormalities.