Automated Analysis of Ultrasound Images to Measure Muscle-Tendon Junction Excursions by Using the Multiple Feature Point Tracking Algorithm.

Ultrasound imaging is used to measure the muscle-tendon junction (MTJ) to investigate the mechanical properties of the tendon and the interaction of the muscle-tendon unit in vivo. Although the MTJ can be observed clearly in the resting state, accurate tracking of the MTJ is difficult during muscle contractions due to changes in its morphology. We devised a novel method using an algorithm that extracts and tracks multiple feature points in ultrasound images to automatically measure the MTJ that moves during muscle contraction. Instead of using a single reference image, multiple feature points are used to improve the tracking performance during the deformation of the MTJ. Subsequently, we experimentally evaluated the usefulness of this method. Tests were conducted on 20 healthy participants performing isometric maximal contractions, and ultrasound echo images of the medial gastrocnemius and Achilles tendon junctions were recorded. MTJ excursion was calculated using the developed multiple feature point algorithm and two conventional methods-multi-updating template-matching and modified Lucas-Kanade (LK)-based on automatic and manual analyses. The root mean square error (RMSE) was used to compare the results. The intraclass correlation coefficient (ICC) was used to evaluate the repeatability among examiners. RMSE was 1.57 ± 0.62 for the proposed algorithm and 2.18 ± 0.89 and 1.84 ± 1.13 for the conventional methods. The Bland-Altman plot showed that the proposed method exhibited a lower 95% confidence interval than the two conventional methods. Thus, the proposed algorithm had the smallest error. Furthermore, the ICC values were 0.96, 0.40, and 0.86 for the proposed algorithm, multi-updating template-matching, and the modified LK method, respectively. When tracking an MTJ excursion that flexibly changes its shape, the use of multiple feature points provides robust results and achieves tracking that approximates the manual analysis results.