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  • DeepACSA [Elektronski vir] : automatic segmentation of cross-sectional area in ultrasound images of lower limb muscles using deep learning
    Ritsche, Paul ...
    Purpose: Muscle anatomical cross-sectional area (ACSA) can be assessed using ultrasound and images are usually evaluated manually. Here, we present DeepACSA, a deep learning approach to automatically ... segment ACSA in panoramic ultrasound images of the human rectus femoris (RF), vastus lateralis (VL), gastrocnemius medialis (GM) and lateralis (GL) muscles. Methods: We trained three muscle-specific convolutional neural networks (CNN) using 1772 ultrasound images from 153 participants (age = 38.2 yr, range = 13–78). Images were acquired in 10% increments from 30% to 70% of femur length for RF and VL and at 30% and 50% of muscle length for GM and GL. During training, CNN performance was evaluated using intersection-over-union scores. We compared the performance of DeepACSA to manual analysis and a semiautomated algorithm using an unseen test set. Results: Comparing DeepACSA analysis of the RF to manual analysis with erroneous predictions removed (3.3%) resulted in intraclass correlation (ICC) of 0.989 (95% confidence interval = 0.983–0.992), mean difference of 0.20 cm2 (0.10–0.30), and SEM of 0.33 cm2 (0.26–0.41). For the VL, ICC was 0.97 (0.96–0.968), mean difference was 0.85 cm2 (−0.4 to 1.31), and SEM was 0.92 cm2 (0.73–1.09) after removal of erroneous predictions (7.7%). After removal of erroneous predictions (12.3%), GM/GL muscles demonstrated an ICC of 0.98 (0.96–0.99), a mean difference of 0.43 cm2 (0.21–0.65), and an SEM of 0.41 cm2 (0.29–0.51). Analysis duration was 4.0 ± 0.43 s (mean ± SD) for analysis of one image in our test set using DeepACSA. Conclusions: DeepACSA provides fast and objective segmentation of lower limb panoramic ultrasound images comparable with manual segmentation. Inaccurate model predictions occurred predominantly on low-quality images, highlighting the importance of high-quality image for accurate prediction.
    Source: Medicine & science in sports & exercise. - ISSN 1530-0315 (Vol. 54, iss. 12, Dec. 2022, str. 2188-2195)
    Type of material - e-article ; adult, serious
    Publish date - 2022
    Language - english
    COBISS.SI-ID - 136572675

    Link(s):

    https://doi.org/10.1249/MSS.0000000000003010

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