Ultrasonography-Based Patellar Tendon Area Measurement: Comparability of Automated vs. Manual Segmentation

Abstract While several openly available tools for the automatic segmentation of the anatomical cross-sectional area (ACSA) of muscle exist, there is no open-source and peer-reviewed tool for the patellar tendon. In this study, we tested an automatic approach for the segmentation of the patellar tendon ACSA in ultrasound images. Images were acquired at 25%, 50%, and 75% of the patellar tendon length from 30 participants (age 46.87 ± 6.03 years; BMI 25.45 ± 4.14 kg/m 2 ). To assess measurement consistency, we evaluated intra-rater and inter-session reliability using manual segmentation of the ACSA. Additionally, we trained three neural networks with a dataset of 497 images to compare manual with automatic segmentation. Intra-rater reliability was found to be good with intraclass correlation coefficient (ICC) of 0.804 (95% CI 0.628–0.902), standard error of measurement (SEM) equal to 0.05 cm 2 (0.03–0.07), and mean absolute error (MAE) of 0.05 cm 2 (0.04–0.07), while inter-session reliability was excellent with ICC of 0.980 (0.970–0.987), SEM equal to 0.02 cm 2 (0.02–0.02), and MAE of 0.02 cm 2 (0.01–0.02). Regarding the comparability with manual analysis after removal of erroneous predictions, ICC was equal to 0.848 (0.702–0.914), SEM was 0.05 (0.04–0.07), and MAE was 0.05 cm 2 (0.05–0.06) with a small standardized mean difference of 0.53 (0.33–0.75). When applying the model, analysis times per image ranged between 0.302 and 0.414 s. The proposed approach enables fast and less operator-dependent patellar tendon ACSA analysis. Although some differences were observed between manual and automatic analysis, this tool, if applied cautiously, could provide valuable support in clinical and research settings.