A Fully Automated 3D CT U-Net Framework for Segmentation and Measurement of the Masseter Muscle, Innovatively...

The segmentation and volume measurement of the masseter muscle play an important role in radiological evaluation. Manual segmentation is considered the gold standard, but it has limited efficiency. This study aims to develop and evaluate a U-Net-based coarse-to-fine learning framework for automated segmentation and volume measurement of the masseter muscle, providing baseline data on muscle characteristics in 840 healthy East Asian volunteers, while introducing a self-supervised algorithm to reduce the sample size required for deep learning.

A database of 840 individuals (253 males, 587 females) with negative head CT scans was utilized. Following G. Power's sample size calculation, 15 cases were randomly chosen for clinical validation. Masseter segmentation was conducted manually in manual group and automatically in Auto-Seg group. The primary endpoint was the masseter muscle volume, while the secondary endpoints included morphological score and runtime, benchmarked against manual segmentation. Reliability tests and paired t tests analyzed intra- and inter-group differences. Additionally, automatic volumetric measurements and asymmetry, calculated as (L - R)/(L±R) × 100%, were evaluated, with the clinical parameter correlation analyzed via Pearson's correlation test.

The volume accuracy of automatic segmentation matched that of manual delineation (P > 0.05), demonstrating equivalence. Manual segmentation's runtime (937.3 ± 95.9 s) significantly surpassed the algorithm's (<1 s, p < 0.001). Among 840 patients, masseter asymmetry was 4.6% ± 4.6%, with volumes of (35.5 ± 9.6) cm³ for adult males and (26.6 ± 7.5) cm3 for adult females.

The U-Net-based algorithm demonstrates high concordance with manual segmentation in delineating the masseter muscle, establishing it as a reliable and efficient tool for CT-based assessments in healthy East Asian populations.

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