Background/Objectives: To evaluate whether semi-automatic and automatic CT-based liver segmentation methods can provide clinically acceptable volumetric agreement compared with manual segmentation while improving processing efficiency in routine practice. Methods: CT images from 86 individuals were retrospectively analyzed. Liver volumes were calculated using manual segmentation, RVX Semi-Automatic, RVX Deep Learning, and TotalSegmentator. Differences among methods were assessed using repeated-measures ANOVA. Agreement with manual segmentation was evaluated using a Bland–Altman analysis, while the Dice Similarity Coefficient (DICE) and Hausdorff Distance (HD) quantified spatial overlap and boundary deviation, respectively. Processing times were recorded. Results: Mean liver volumes were 1503.9 ± 356.0 cm³ (manual), 1512.6 ± 373.6 cm³ (RVX Semi-Automatic), 1549.8 ± 367.9 cm³ (RVX Deep Learning), and 1518.3 ± 365.8 cm³ (TotalSegmentator). RVX Deep Learning produced significantly higher volumes compared with manual segmentation (p < 0.001), whereas RVX Semi-Automatic and TotalSegmentator showed no significant differences (p > 0.05). DICE values were 0.911 ± 0.032, 0.946 ± 0.018, and 0.938 ± 0.021 for RVX Semi-Automatic, RVX Deep Learning, and TotalSegmentator, respectively. HD values were highest for the deep learning-based method. Processing times were shortest for RVX Deep Learning and longest for manual segmentation. Conclusions: Semi-automatic and automatic liver segmentation methods substantially reduce processing time while maintaining clinically acceptable volumetric agreement. Among the evaluated approaches, TotalSegmentator showed the closest agreement with manual segmentation, supporting its use in routine CT-based liver volumetry. Deep learning-based segmentation, although faster, tended to overestimate volume, potentially limiting its use in applications requiring high volumetric precision.

Aim: This study aimed to investigate the acute effect of kinesio taping applied to athletes and sedentary individuals on vertical and horizontal jumping performances. Methods: The study included 20 licensed male basketball players and 20 male sedentary individuals between the ages of 18-25. Vertical jump height and horizontal jump distance were evaluated. Free jumping and squat jumping tests were applied to measure the vertical jump height. Single leg hop test was chosen for the measurement of horizontal jump distance. Single leg hop test was performed with the dominant foot. Tests after kinesio taping were performed 10 minutes after facilitation technique was applied to gastrocnemius muscle. Results: A comparison of the pre- and post-taping data in the athlete group revealed a significant increase in both squat jump and single leg hop tests (p<0.05). In the sedentary group, there was a significant increase only in the single leg hop test compared to before taping (p0.05). In terms of the performance increases after kinesio taping, the increases in the athlete group were higher for all tests compared to the sedentary group (p>0.05). Conclusions: Our study revealed that kinesio taping can improve jumping performance in both athletes and sedentary individuals. We think that kinesio taping could improve the performance especially in sports where jumping movements are frequently used such as basketball.