Muscle geometry and its relevance for sports performance? A perspective of current findings and future opportunities
Authors: Ritsche Paul , Franchi Martino , Spörri Jörg , Keller Martin , Cronin Neil , Faude Oliver 
Published: February 2024

Introduction
Lower limb muscle strength is an important predictor of sports performance, injury risk and frailty in ageing. The strength of a muscle is determined by its geometry and neuronal factors. Muscle geometry can be subdivided into architecture and morphology. Muscle morphology describes shape characteristics such as anatomical cross-sectional area (ACSA), thickness or volume (Maden-Wilkinson et al., 2021). Muscle architecture is determined by muscle fascicle length and the insertion angle of the muscle fascicles in the aponeuroses  and describes the orientation of the muscle fibers relative to their force generation axis (Lieber & Friden, 2000). Muscle geometry is associated to physical performance and strength in humans (Maden-Wilkinson et al., 2021; Werkhausen et al., 2022) and is therefore a main research interest. A cost-effective and participant friendly method to validly and reliably assess muscle geometry is ultrasonography. However, a major limitation of ultrasonography is the subjectivity of image acquisition and the time-consuming image analysis (Ritsche et al., 2021; Ritsche, Wirth, et al., 2022; Ritsche et al., 2023). Moreover, image characteristics are massively influenced by the ultrasonography device used (Ritsche, Schmid, et al., 2022) as well as the muscle region scanned (Monte & Franchi, 2023). This poses constraints on the generalizability of existing automated image analysis approaches. The goal of this series of studies is therefore to optimize the ultrasonography acquisition and data analysis procedures by developing open-source software packages. Secondly, we aim to apply these methods in a sports performance context and describe the relevance of muscle geometry.
Methods
To streamline the time-consuming and subjective process of image analysis, we developed open-source and user-friendly software packages for muscle geometry analysis in lower limb muscles. We developed a semi-automated algorithm “ACSAuto” for assisted analysis of muscle ACSA using common image filtering processes (Ritsche et al., 2021). Given the limited generalizability and required user input of this approach, we developed two fully automated software applications, “DeepACSA” and “DL_Track_US”, using convolutional neural networks for more time efficient and robust analysis of lower limb muscle geometry (Ritsche et al., 2023; Ritsche, Wirth, et al., 2022; Ritsche et al., in press). We compared the predictions in an unseen test set to the current state-of-the-art, manual analysis, in order to evaluate the performance of our algorithms.
To broaden the application of ultrasonography for evaluating muscle geometry in a sports context, we investigated the validity of a low-cost mobile ultrasonography device compared to a high-end counterpart in assessing various muscle architectural parameters in healthy adults (Ritsche, Schmid, et al., 2022).The mobile ultrasonography setup consisted of a smartphone and a portable probe, enabling practitioners high flexibility in the assessment of muscle architecture.
We further investigated the link between muscle geometry and performance among soccer players. In one study, we focused on the m. biceps femoris long head in under-13 to under-15 youth players, assessing architecture and morphology at the mid-muscle point and correlating these with their sprint times and maximum velocity (Ritsche et al., 2020). In a further study, we analyzed the mm. vastus lateralis and rectus femoris in both youth and adult players of both sexes, evaluating muscle geometry at various muscle lengths alongside their knee extension strength during isometric and isokinetic conditions (Ritsche et al., in preparation and under review).
Results
Both ACSAuto and DeepACSA showed high comparability in assessing lower limb muscle ACSA with standard error of measurement lower than one cm2 (SEM ranging from 1.2 to 9.5%; Ritsche et al., 2021; Ritsche, Wirth, et al., 2022). Moreover, DeepACSA provided fast and objective analysis comparable to manual segmentation with no supervision of the analysis process needed. The time needed for analysis was reduced by a factor of 10. DL_Track_US demonstrated high comparability to manual muscle architecture analysis of images and videos, i.e. dynamic situations, (Ritsche et al., 2023; Ritsche et al., in press) and a reduction in the duration of analysis by a factor of 100.
The mobile ultrasonography system showed a high degree of reliability and comparability only for m. gastrocnemius medialis architecture assessment, with a standard error of measurement lower than 10% for all architectural parameters (Ritsche, Schmid, et al., 2022). Thus, its reliability and comparability depended on the muscle assessed.
We observed relevant correlations between muscle ACSA in young and adult male soccer players as well as in female soccer players and performance (Ritsche et al., 2020; Ritsche et al., unpublished). Moreover, we observed changes in muscle geometry with age and differences between males and females. Specifically, m. biceps femoris ACSA was strongly correlated with 30m sprint times and maximal velocity (r = -0.61 and r = 0.61, respectively), highlighting its importance in athletic performance (Ritsche et al., 2020). M. vastus lateralis ACSA at 50% of muscle length was most frequently related to knee extension strength (r = 0.40 - 0.53), which was observed in both sexes and across several age groups of male soccer players (Ritsche et al., in preparation and under review). Relevant correlations occurred more frequently in older age groups and higher knee extension velocities. Interestingly, we did not observe relevant correlations between muscle architecture and performance in the mm. biceps femoris and vastus lateralis.
Discussion/Conclusion
The results of this series studies so far led to three main insights. Firstly, the development of the “ACSAuto”, “DeepACSA” and “DL_Track_US” tools, utilizing semi-automated and fully automated analysis techniques applying deep learning algorithms, marked another step forward in overcoming the subjectivity and time consuming image evaluation. In a user-friendly way, these tools enable reproducible and objective analyses of muscle geometry in ultrasonography images. Secondly, with technological advancements, assessing muscle geometry with ultrasonography is possible using a smartphone and a probe, and often gives comparable results to high-end devices (Ritsche, Schmid, et al., 2022). This allows for a broader and more versatile application of muscle geometry assessment. However, our results highlight the need for a selective approach based on the muscle group being assessed and technical improvements of existing devices. Lastly, our findings across several investigations reveal a relevant positive correlation between muscle ACSA and performance metrics such as sprint times and knee extension strength (Ritsche et al., 2020; Ritsche et al., unpublished), corroborating previous research (Maden-Wilkinson et al., 2021; Monte & Franchi, 2023). The relationship was more pronounced in older age groups, suggesting that muscle geometry's influence on performance may amplify with athletic maturity. Apart from that, we observed the relationship in the m. vastus lateralis to be region- and contraction velocity-dependent. In agreement with Werkhausen et al. (2022), no relation of muscle architecture with strength when assessed in a static resting position was observed. This highlights the need for a potential shift towards assessing changes in muscle geometry during contraction rather than in static situations when evaluating the relation between muscle geometry and performance.
Finally, remaining challenges include the comparability of muscle geometry assessment in the literature, the analysis methods used and the low generalizability of available automated analysis approaches (ours included). There is a clear need for methodological consensus on the assessment of muscle geometry when using ultrasonography, and more versatile analysis approaches are needed to enable an easy, generalizable and reproducible analysis of images and videos.
Therefore, future works should target to establish assessment and analysis guidelines of muscle geometry in ultrasonography images to increase the comparability and reproducibility of results. Moreover, assessing changes in muscle geometry during contraction rather than during rest should be focused.
References
Lieber, R. L., & Friden, J. (2000). Functional and clinical significance of skeletal muscle architecture. Muscle Nerve, 23(11), 1647–1666. https://doi.org/10.1002/1097-4598(200011)23:11%3C1647::aid-mus1%3E3.0.co;2-m
Maden-Wilkinson, T. M., Balshaw, T. G., Massey, G. J., & Folland, J. P. (2021). Muscle architecture and morphology as determinants of explosive strength. European Journal of Applied Physiology, 121(4), 1099–1110. https://doi.org/10.1007/s00421-020-04585-1
Monte, A., & Franchi, M. V. (2023). Regional muscle features and their association with knee extensors force production at a single joint angle. European Journal of Applied Physiology, 123, 2239-2248. https://doi.org/10.1007/s00421-023-05237-w
Ritsche, P., Bernhard, T., Roth, R., Lichtenstein, E., Keller, M., Zingg, S., Franchi, M. V., & Faude, O. (2020). M. biceps femoris long head architecture and sprint ability in youth soccer players. International Journal of Sports Physiology and Performance, 16(11), 1616-1624. https://doi.org/10.1123/ijspp.2020-0726
Ritsche, P., Schmid, R., Franchi, M. V., & Faude, O. (2022). Agreement and reliability of lower limb muscle architecture measurements using a portable ultrasound device. Frontiers in Physiology, 13, Article 981862. https://doi.org/10.3389/fphys.2022.981862
Ritsche, P., Seynnes, O., & Cronin, N. (2023). DL_Track_US: A python package to analyse muscleultrasonography images. Journal of Open Source Software, 8(85), Article 5206. https://doi.org/10.21105/joss.05206
Ritsche, P., Wirth, P., Cronin, N. J., Sarto, F., Narici, M. V., Faude, O., & Franchi, M. V. (2022). DeepACSA: Automatic segmentation of cross-sectional area in ultrasound images of lower limb muscles using deep learning. Medicine & Science in Sports & Exercise, 54(12), 2188-2195. https://doi.org/10.1249/MSS.0000000000003010
Ritsche, P., Wirth, P., Franchi, M. V., & Faude, O. (2021). ACSAuto-semi-automatic assessment of human vastus lateralis and rectus femoris cross-sectional area in ultrasound images. Scientific Reports, 11, Article 13042. https://doi.org/10.1038/s41598-021-92387-6
Werkhausen, A., Gløersen, Ø., Nordez, A., Paulsen, G., Bojsen-Møller, J., & Seynnes, O. R. (2022). Rate of force development relationships to muscle architecture and contractile behavior in the human vastus lateralis. Scientific Reports, 12, Article 21816. https://doi.org/10.1038/s41598-022-26379-5