MuscleExpert: a semi-automated tool delivering ImageJ-comparable measurements of muscle thickness, area, and quality
Zongpan Li, Li-Qun Zhang, Marcel Bahia Lanza · PeerJ · 2026
Background: Most image-analysis software programs were not designed specifically for ultrasound-based measurements, resulting in irrelevant general-
Purpose: features that force users to navigate through multiple menus and making the analysis time-consuming and increasing the likelihood of error. The
Aim: of this study was to present and validate the semi-automated MuscleExpert software against the widely used ImageJ by comparing muscle thickness (MT), cross-sectional area (CSA), and muscle quality (MQ-measured as echo intensity).
Methods: the two-one-sided tests (TOST) procedure; the reliability between means was quantified by an intraclass correlation coefficient (ICC); the precision was expressed as the coefficient of variation (CV) and standard error of measurement (SEM); and agreement was assessed using Bland-Altman plots.
Results: The TOST for MT, CSA, and MQ were within their respective equivalence margins. The ICC was excellent for all measures (MT: ICC = 0.999; CSA: ICC = 0.999; MQ: ICC = 1.000). Precision was similarly high, with CVs of 0.49% (MT), 0.54% (CSA), and 0.33% (MQ), demonstrating minimal variability between the two
methods. SEM values were low across all outcomes, corresponding to 0.49% (MT), 0.53% (CSA), and 0.23% (MQ). Finally, the Bland-Altman analysis demonstrated minimal systematic differences and no proportional bias between ImageJ and MuscleExpert for all metrics. The present
Findings: indicate that MuscleExpert and ImageJ produce nearly identical
results for MT, CSA, and MQ. These
results for MT, CSA, and MQ. These findings support the integration of MuscleExpert into clinical and research workflows, offering a more efficient solution for muscle assessment without compromising measurement integrity.