Tutorial: Analysis of central and peripheral motor unit properties from decomposed High-Density surface EMG signals with openhdemg.

High-Density surface Electromyography (HD-sEMG) is the most established technique for the non-invasive analysis of single motor unit (MU) activity in humans. It provides the possibility to study the central properties (e.g., discharge rate) of large populations of MUs by analysis of their firing pattern. Additionally, by spike-triggered averaging, peripheral properties such as MUs conduction velocity can be estimated over adjacent regions of the muscles and single MUs can be tracked across different recording sessions. In this tutorial, we guide the reader through the investigation of MUs properties from decomposed HD-sEMG recordings by providing both the theoretical knowledge and practical tools necessary to perform the analyses. The practical application of this tutorial is based on openhdemg, a free and open-source community-based framework for the automated analysis of MUs properties built on Python 3 and composed of different modules for HD-sEMG data handling, visualisation, editing, and analysis. openhdemg is interfaceable with most of the available recording software, equipment or decomposition techniques, and all the built-in functions are easily adaptable to different experimental needs. The framework also includes a graphical user interface which enables users with limited coding skills to perform a robust and reliable analysis of MUs properties without coding.

Fully Automated Analysis of Muscle Architecture from B-Mode Ultrasound Images with DL_Track_US.

OBJECTIVE: B-mode ultrasound can be used to image musculoskeletal tissues, but one major bottleneck is analyses of muscle architectural parameters (i.e., muscle thickness, pennation angle and fascicle length), which are most often performed manually. METHODS: In this study we trained two different neural networks (classic U-Net and U-Net with VGG16 pre-trained encoder) to detect muscle fascicles and aponeuroses using a set of labeled musculoskeletal ultrasound images. We determined the best-performing model based on intersection over union and loss metrics. We then compared neural network predictions on an unseen test set with those obtained via manual analysis and two existing semi/automated analysis approaches (simple muscle architecture analysis [SMA] and UltraTrack). DL_Track_US detects the locations of the superficial and deep aponeuroses, as well as multiple fascicle fragments per image. RESULTS: For single images, DL_Track_US yielded results similar to those produced by a non-trainable automated method (SMA; mean difference in fascicle length: 5.1 mm) and human manual analysis (mean difference: -2.4 mm). Between-method differences in pennation angle were within 1.5°, and mean differences in muscle thickness were less than 1 mm. Similarly, for videos, there was overlap between the results produced with UltraTrack and DL_Track_US, with intraclass correlations ranging between 0.19 and 0.88. CONCLUSION: DL_Track_US is fully automated and open source and can estimate fascicle length, pennation angle and muscle thickness from single images or videos, as well as from multiple superficial muscles. We also provide a user interface and all necessary code and training data for custom model development.

Agreement and reliability of lower limb muscle architecture measurements using a portable ultrasound device.

High end ultrasonography devices lack in portability and are expensive. We investigated the agreement and reliability of a handheld and portable ultrasound system for human lower limb muscle architecture measurements. We captured ultrasound images of the rectus femoris (RF), vastus lateralis (VL) and gastrocnemius medialis (GM) in 36 active healthy participants (15 female, 21 male) at 50% of muscle length using the handheld Lumify (L12-4, linear-array 37 mm, Philips Healthcare, Amsterdam, Netherlands) and a high-end laboratory device (ACUSON Juniper, linear-array 54 mm, 12L3, SIEMENS Healthineers, Erlangen, Germany). We compared measurements of muscle fascicle length, pennation angle and thickness. To assess inter-session reliability of the Lumify system, participants were measured twice within 1 week. Comparing RF architecture measurements of both devices resulted in intra-class correlations (ICCs) ranging from 0.46-0.82 and standardized mean difference (SMDs) ranging from -0.45-0.05. For VL, ICCs ranged from 0.60-0.89 and SMDs ranged from -0.11-0.13. ICCs and SMDs for the GM ranged from 0.82-0.86 and -0.07-0.07. Calculating inter-session reliability for RF resulted in ICCs ranging from 0.44-0.76 and SMDs ranging from -0.38-0.15. For VL, ICCs and SMDs ranged from 0.57-0.75 and -0.13-0.02. ICCs for GM ranged from 0.75-0.92 and SMDs ranged from -0.15-0.16. Measurement of muscle thickness demonstrated the highest agreement (ICC ≥0.82) and reliability (ICC ≥0.75) across all muscles. The Lumify system was comparable to a high-end device and reliable for GM measurements. However, agreement and reliability were lower for the RF and VL. Of all evaluated architectural parameters, muscle thickness exhibited highest agreement and reliability.

DeepACSA: Automatic Segmentation of Cross-Sectional Area in Ultrasound Images of Lower Limb Muscles Using Deep Learning.

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 cm 2 (0.10-0.30), and SEM of 0.33 cm 2 (0.26-0.41). For the VL, ICC was 0.97 (0.96-0.968), mean difference was 0.85 cm 2 (-0.4 to 1.31), and SEM was 0.92 cm 2 (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 cm 2 (0.21-0.65), and an SEM of 0.41 cm 2 (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.

ACSAuto-semi-automatic assessment of human vastus lateralis and rectus femoris cross-sectional area in ultrasound images.

Open-access scripts to perform muscle anatomical cross-sectional area (ACSA) evaluation in ultrasound images are currently unavailable. This study presents a novel semi-automatic ImageJ script (named "ACSAuto") for quantifying the ACSA of lower limb muscles. We compared manual ACSA measurements from 180 ultrasound scans of vastus lateralis (VL) and rectus femoris (RF) muscles to measurements assessed by the ACSAuto script. We investigated inter- and intra-investigator reliability of the script. Consecutive-pairwise intra-class correlations (ICC) and standard error of measurement (SEM) with 95% compatibility interval were calculated. Bland-Altman analyses were employed to test the agreement between measurements. Comparing manual and ACSAuto measurements, ICCs and SEMs ranged from 0.96 to 0.999 and 0.12 to 0.96 cm2 (1.2-5.9%) and mean bias was smaller than 0.5 cm2 (4.3%). Inter-investigator comparison revealed ICCs, SEMs and mean bias ranging from 0.85 to 0.999, 0.07 to 1.16 cm2 (0.9-7.6%) and - 0.16 to 0.66 cm2 (- 0.6 to 3.2%). Intra-investigator comparison revealed ICCs, SEMs and mean bias between 0.883-0.998, 0.07-0.93 cm2 (1.1-7.6%) and - 0.80 to 0.15 cm2 (- 3.4 to 1.8%). Image quality needs to be high for efficient and accurate ACSAuto analyses. Taken together, the ACSAuto script represents a reliable tool to measure RF and VL ACSA, is comparable to manual analysis and can reduce time needed to evaluate ultrasound images.

M. Biceps Femoris Long Head Architecture and Sprint Ability in Youth Soccer Players.

PURPOSE: Hamstring muscle architecture may be associated with sprint performance and the risk of sustaining a muscle injury, both of which increase during puberty. In this study, we investigated the m. biceps femoris long head (BFlh) cross-sectional area (ACSA), fascicle length (FL) and pennation angle (PA), and sprint performance as well as their relationship in under 13 to 15 youth soccer players. METHODS: We measured 85 players in under-13 (n = 29, age = 12.5 [0.1] y, height = 155.3 [6.2] cm, weight = 43.9 [7.6] kg), under-14 (n = 25, age = 13.5 [0.3] y, height = 160.6 [7.7] cm, weight = 47.0 [6.8] kg), and under-15 (n = 31, age = 14.4 [0.3] y, height = 170.0 [7.7] cm, weight = 58.1 [8.8] kg) teams. We used ultrasound to measure BFlh ACSA, FL and PA, and sprint tests to assess 10- and 30-m sprint time, maximal velocity  (vmax), and maximal acceleration (αmax). We calculated Pearson r to assess the relationship between sprint ability and architectural parameters. RESULTS: All muscle architectural parameters increased from the under-13 to the under-15 age group (BFlh ACSA = 37%, BFlh FL = 11%, BFlh PA = 8%). All sprint performance parameters improved from the under-13 to under-15 age categories (30-m time = 7%, 10-m time = 4%, vmax = 9%, αmax = 7%). The BFlh ACSA was correlated with 30-m sprint time (r = -.61 (95% compatibility interval [CI] [-.73, -.45]) and vmax (r = .61, 95% CI [.45, .72]). A combination of BFlh ACSA and age best predicted 30-m time (R² = .47 [.33, .62]) and 10-m time (R² = .23 [.08, .38]). CONCLUSIONS: Muscle architectural as well as sprint performance parameters increase from the under-13 to under-15 age groups. Even though we found correlations for all assessed architectural parameters, BFlh ACSA was best related to the assessed sprint parameters.