Fully Automated Muscle Ultrasound Analysis (MUSA): Robust and Accurate Muscle Thickness Measurement

Duchenne and Becker muscular dystrophy: Cellular mechanisms, image analysis, and computational models: A review

The muscle is the principal tissue that is capable to transform potential energy into kinetic energy. This process is due to the transformation of chemical energy into mechanical energy to enhance the movements and all the daily activities. However, muscular tissues can be affected by some pathologies associated with genetic alterations that affect the expression of proteins. As the muscle is a highly organized structure in which most of the signaling pathways and proteins are related to one another, pathologies may overlap. Duchenne muscular dystrophy (DMD) is one of the most severe muscle pathologies triggering degeneration and muscle necrosis. Several mathematical models have been developed to predict muscle response to different scenarios and pathologies. The aim of this review is to describe DMD and Becker muscular dystrophy in terms of cellular behavior and molecular disorders and to present an overview of the computational models implemented to understand muscle behavior with the aim of improving regenerative therapy.

MyoVision-US: an Artificial Intelligence-Powered Software for Automated Analysis of Skeletal Muscle Ultrasonography

Introduction/Aims

Muscle ultrasound has high utility in clinical practice and research; however, the main challenges are the training and time required for manual analysis to achieve objective quantification of morphometry. This study aimed to develop and validate a software tool powered by artificial intelligence (AI) by measuring its consistency and predictability of expert manual analysis quantifying lower limb muscle ultrasound images across healthy, acute, and chronic illness subjects.

Methods

Quadriceps complex (QC [rectus femoris and vastus intermedius]) and tibialis anterior (TA) muscle ultrasound images of healthy, intensive care unit, and/or lung cancer subjects were captured with portable devices. Automated analyses of muscle morphometry were performed using a custom-built deep-learning model (MyoVision-US), while manual analyses were performed by experts. Consistency between manual and automated analyses was determined using intraclass correlation coefficients (ICC), while predictability of MyoVision -US was calculated using adjusted linear regression (adj.R 2 ).

Results

Manual analysis took approximately 24 hours to analyze all 180 images, while MyoVision - US took 247 seconds, saving roughly 99.8%. Consistency between the manual and automated analyses by ICC was good to excellent for all QC (ICC:0.85-0.99) and TA (ICC:0.93-0.99) measurements, even for critically ill (ICC:0.91-0.98) and lung cancer (ICC:0.85-0.99) images. The predictability of MyoVision-US was moderate to strong for QC (adj.R 2 :0.56-0.94) and TA parameters (adj.R 2 :0.81-0.97).

Discussion

The application of AI automating lower limb muscle ultrasound analyses showed excellent consistency and strong predictability compared with human analysis. Future work needs to explore AI-powered models for the evaluation of other skeletal muscle groups.

Changes in the mechanical properties of the thigh and lower leg muscle-tendon units during the early follicular and early luteal phases

Background

This study aimed to determine changes in the muscle and tendon stiffness of the thigh and lower leg muscle-tendon units during the early follicular and early luteal phases, and check for possible relations between muscle and tendon stiffness in each phase.

Methods

The sample consisted of 15 female university students with regular menstrual cycles. The basal body temperature method, ovulation kit, and salivary estradiol concentration measurement were used to estimate the early follicular and early luteal phases. A portable digital palpation device measured muscle-tendon stiffness in the early follicular and early luteal phases. The measurement sites were the rectus femoris (RF), vastus medialis (VM), patellar tendon (PT), medial head of gastrocnemius muscle, soleus muscle, and Achilles tendon.

Results

No statistically significant differences in the thigh and lower leg muscle-tendon unit stiffness were seen between the early follicular and early luteal phases. Significant positive correlations were found between the stiffness of the RF and PT (r = 0.608, p = 0.016) and between the VM and PT (r = 0.737, p = 0.002) during the early luteal phase.

Conclusion

The present results suggest that the stiffness of leg muscle-tendon units of the anterior thigh and posterior lower leg do not change between the early follicular and early luteal phases and that tendons may be stiffer in those women who have stiffer anterior thigh muscles during the early luteal phase.

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.

Neuromuscular Ultrasound in Intensive Care Unit-Acquired Weakness: Current State and Future Directions

Intensive care unit-acquired weakness (ICUAW) is one of the most common causes of muscle atrophy and functional disability in critically ill intensive care patients. Clinical examination, manual muscle strength testing and monitoring are frequently hampered by sedation, delirium and cognitive impairment. Many different attempts have been made to evaluate alternative compliance-independent methods, such as muscle biopsies, nerve conduction studies, electromyography and serum biomarkers. However, they are invasive, time-consuming and often require special expertise to perform, making them vastly impractical for daily intensive care medicine. Ultrasound is a broadly accepted, non-invasive, bedside-accessible diagnostic tool and well established in various clinical applications. Hereby, neuromuscular ultrasound (NMUS), in particular, has been proven to be of significant diagnostic value in many different neuromuscular diseases. In ICUAW, NMUS has been shown to detect and monitor alterations of muscles and nerves, and might help to predict patient outcome. This narrative review is focused on the recent scientific literature investigating NMUS in ICUAW and highlights the current state and future opportunities of this promising diagnostic tool.

Automatic Analysis of Transverse Musculoskeletal Ultrasound Images Based on the Multi-Task Learning Model

Musculoskeletal ultrasound imaging is an important basis for the early screening and accurate treatment of muscle disorders. It allows the observation of muscle status to screen for underlying neuromuscular diseases including myasthenia gravis, myotonic dystrophy, and ankylosing muscular dystrophy. Due to the complexity of skeletal muscle ultrasound image noise, it is a tedious and time-consuming process to analyze. Therefore, we proposed a multi-task learning-based approach to automatically segment and initially diagnose transverse musculoskeletal ultrasound images. The method implements muscle cross-sectional area (CSA) segmentation and abnormal muscle classification by constructing a multi-task model based on multi-scale fusion and attention mechanisms (MMA-Net). The model exploits the correlation between tasks by sharing a part of the shallow network and adding connections to exchange information in the deep network. The multi-scale feature fusion module and attention mechanism were added to MMA-Net to increase the receptive field and enhance the feature extraction ability. Experiments were conducted using a total of 1827 medial gastrocnemius ultrasound images from multiple subjects. Ten percent of the samples were randomly selected for testing, 10% as the validation set, and the remaining 80% as the training set. The results show that the proposed network structure and the added modules are effective. Compared with advanced single-task models and existing analysis methods, our method has a better performance at classification and segmentation. The mean Dice coefficients and IoU of muscle cross-sectional area segmentation were 96.74% and 94.10%, respectively. The accuracy and recall of abnormal muscle classification were 95.60% and 94.96%. The proposed method achieves convenient and accurate analysis of transverse musculoskeletal ultrasound images, which can assist physicians in the diagnosis and treatment of muscle diseases from multiple perspectives.

Muscle Cross-Sectional Area Segmentation in Transverse Ultrasound Images Using Vision Transformers

Automatically measuring a muscle’s cross-sectional area is an important application in clinical practice that has been studied extensively in recent years for its ability to assess muscle architecture. Additionally, an adequately segmented cross-sectional area can be used to estimate the echogenicity of the muscle, another valuable parameter correlated with muscle quality. This study assesses state-of-the-art convolutional neural networks and vision transformers for automating this task in a new, large, and diverse database. This database consists of 2005 transverse ultrasound images from four informative muscles for neuromuscular disorders, recorded from 210 subjects of different ages, pathological conditions, and sexes. Regarding the reported results, all of the evaluated deep learning models have achieved near-to-human-level performance. In particular, the manual vs. the automatic measurements of the cross-sectional area exhibit an average discrepancy of less than 38.15 mm2, a significant result demonstrating the feasibility of automating this task. Moreover, the difference in muscle echogenicity estimated from these two readings is only 0.88, another indicator of the proposed method’s success. Furthermore, Bland−Altman analysis of the measurements exhibits no systematic errors since most differences fall between the 95% limits of agreements and the two readings have a 0.97 Pearson’s correlation coefficient (p < 0.001, validation set) with ICC (2, 1) surpassing 0.97, showing the reliability of this approach. Finally, as a supplementary analysis, the texture of the muscle’s visible cross-sectional area was examined using deep learning to investigate whether a classification between healthy subjects and patients with pathological conditions solely from the muscle texture is possible. Our preliminary results indicate that such a task is feasible, but further and more extensive studies are required for more conclusive results.

Automatic Extraction of Muscle Parameters with Attention UNet in Ultrasonography

Automatically delineating the deep and superficial aponeurosis of the skeletal muscles from ultrasound images is important in many aspects of the clinical routine. In particular, finding muscle parameters, such as thickness, fascicle length or pennation angle, is a time-consuming clinical task requiring both human labour and specialised knowledge. In this study, a multi-step solution for automating these tasks is presented. A process to effortlessly extract the aponeurosis for automatically measuring the muscle thickness has been introduced as a first step. This process consists mainly of three parts. In the first part, the Attention UNet has been incorporated to automatically delineate the boundaries of the studied muscles. Afterwards, a specialised post-processing algorithm was utilised to improve (and correct) the segmentation results. Lastly, the calculation of the muscle thickness was performed. The proposed method has achieved similar to a human-level performance. In particular, the overall discrepancy between the automatic and the manual muscle thickness measurements was equal to 0.4 mm, a significant result that demonstrates the feasibility of automating this task. In the second step of the proposed methodology, the fascicle’s length and pennation angle are extracted through an unsupervised pipeline. Initially, filtering is applied to the ultrasound images to further distinguish the tissues from the other muscle structures. Later, the well-known K-Means algorithm is used to isolate them successfully. As the last step, the dominant angle of the segmented muscle tissues is reported and compared with manual measurements. The proposed pipeline is showing very promising results in the evaluated dataset. Specifically, in the calculation of the pennation angle, the overall discrepancy between the automatic and the manual measurements was less than 2.22° (degrees), once more comparable with the human-level performance. Finally, regarding the fascicle length measurements, the results were divided based on the muscle properties. In the muscles where a large portion (or all) of the fascicles are located between the upper and lower aponeuroses, the proposed pipeline exhibits superb performance; otherwise, overall accuracy deteriorates due to errors caused by the trigonometric approximations needed for the length calculation.

Fully Automatic Analysis of Muscle B-Mode Ultrasound Images Based on the Deep Residual Shrinkage U-Net

The parameters of muscle ultrasound images reflect the function and state of muscles. They are of great significance to the diagnosis of muscle diseases. Because manual labeling is time-consuming and laborious, the automatic labeling of muscle ultrasound image parameters has become a research topic. In recent years, there have been many methods that apply image processing and deep learning to automatically analyze muscle ultrasound images. However, these methods have limitations, such as being non-automatic, not applicable to images with complex noise, and only being able to measure a single parameter. This paper proposes a fully automatic muscle ultrasound image analysis method based on image segmentation to solve these problems. This method is based on the Deep Residual Shrinkage U-Net(RS-Unet) to accurately segment ultrasound images. Compared with the existing methods, the accuracy of our method shows a great improvement. The mean differences of pennation angle, fascicle length and muscle thickness are about 0.09°, 0.4 mm and 0.63 mm, respectively. Experimental results show that the proposed method realizes the accurate measurement of muscle parameters and exhibits stability and robustness.

Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment

Ultrasound imaging is a patient-friendly and robust technique for studying physiological and pathological muscles. An automatic deep learning (DL) system for the analysis of ultrasound images could be useful to support an expert operator, allowing the study of large datasets requiring less human interaction. The purpose of this study is to present a deep learning algorithm for the cross-sectional area (CSA) segmentation in transverse musculoskeletal ultrasound images, providing a quantitative grayscale analysis which is useful for studying muscles, and to validate the results in a large dataset. The dataset included 3917 images of biceps brachii, tibialis anterior and gastrocnemius medialis acquired on 1283 subjects (mean age 50 ± 21 years, 729 male). The algorithm was based on multiple deep-learning architectures, and its performance was compared to a manual expert segmentation. We compared the mean grayscale value inside the automatic and manual CSA using Bland-Altman plots and a correlation analysis. Classification in healthy and abnormal muscles between automatic and manual segmentation were compared using the grayscale value z-scores. In the test set, a Precision of 0.88 ± 0.12 and a Recall of 0.92 ± 0.09 was achieved. The network segmentation performance was slightly less in abnormal muscles, without a loss of discrimination between healthy and abnormal muscle images. Bland-Altman plots showed no clear trend in the error distribution and the two readings have a 0.99 Pearson's correlation coefficient (p < 0.001, test set). The ICC(A, 1) calculated between the z-score readings was 0.99. The algorithm achieves robust CSA segmentation performance and gives mean grayscale level information comparable to a manual operator. This could provide a helpful tool for clinicians in neuromuscular disease diagnosis and follow-up. The entire dataset and code are made available for the research community.

Automatic Measurement of Pennation Angle from Ultrasound Images using Resnets

In this study, an automatic pennation angle measuring approach based on deep learning is proposed. Firstly, the Local Radon Transform (LRT) is used to detect the superficial and deep aponeuroses on the ultrasound image. Secondly, a reference line are introduced between the deep and superficial aponeuroses to assist the detection of the orientation of muscle fibers. The Deep Residual Networks (Resnets) are used to judge the relative orientation of the reference line and muscle fibers. Then, reference line is revised until the line is parallel to the orientation of the muscle fibers. Finally, the pennation angle is obtained according to the direction of the detected aponeuroses and the muscle fibers. The angle detected by our proposed method differs by about 1° from the angle manually labeled. With a CPU, the average inference time for a single image of the muscle fibers with the proposed method is around 1.6 s, compared to 0.47 s for one of the image of a sequential image sequence. Experimental results show that the proposed method can achieve accurate and robust measurements of pennation angle.

Assessment methods of vastus lateralis muscle architecture using panoramic ultrasound: a new approach, test-retest reliability and measurement error

Abstract Extended-field-of-view ultrasonography is a valid alternative to determine the dimensions of the skeletal striated muscle; however, some factors may influence the final measurement. The aim of this study was to determine the test-retest reliability and measurement error of vastus lateralis muscle architecture variables through internal anatomical landmarks and to compare three fixed determined points using extended-field-of-view ultrasonography. Twelve young (24 ± 6 years) adult university male students participated in the study. Images were obtained through extended-field-of-view ultrasonography of the vastus lateralis muscle. Measurements were made for muscle thickness (MT), fascicle length (FL), and fascicle pennation angle (FA) using a method that identifies internal anatomical landmarks. MT was also measured at predetermined distances of 2 cm proximal, 6 cm proximal, and 2 cm distal. One-way ANOVA with repeated measures did not identify any test-retest significant differences for all variables measured. Typical measurement error in centimeters (cm) or degrees (º), coefficient of variation in percentage (%) and intraclass correlation coefficient were MT = 0.07 cm, 2.93%, 0.964; FL = 0.31 cm, 2.89%, 0.947; FA = 0.92°, 4.08%, 0.942; MT 2 cm proximal = 0.10 cm, 3.77%, 0.910; MT 6 cm proximal = 0.27 cm, 9.66%, 0.576; MT 2 cm distal = 0.35 cm, 19.76%, 0.564. MT, FL and FA showed high reliability and low measurement error. Internal anatomical landmarks proved to be more reliable and presented smaller measurement errors when compared to the predetermined distances method.

Using deep learning to generate synthetic B-mode musculoskeletal ultrasound images

BACKGROUND AND OBJECTIVE

Deep learning approaches are common in image processing, but often rely on supervised learning, which requires a large volume of training images, usually accompanied by hand-crafted labels. As labelled data are often not available, it would be desirable to develop methods that allow such data to be compiled automatically. In this study, we used a Generative Adversarial Network (GAN) to generate realistic B-mode musculoskeletal ultrasound images, and tested the suitability of two automated labelling approaches.

METHODS

We used a model including two GANs each trained to transfer an image from one domain to another. The two inputs were a set of 100 longitudinal images of the gastrocnemius medialis muscle, and a set of 100 synthetic segmented masks that featured two aponeuroses and a random number of 'fascicles'. The model output a set of synthetic ultrasound images and an automated segmentation of each real input image. This automated segmentation process was one of the two approaches we assessed. The second approach involved synthesising ultrasound images and then feeding these images into an ImageJ/Fiji-based automated algorithm, to determine whether it could detect the aponeuroses and muscle fascicles.

RESULTS

Histogram distributions were similar between real and synthetic images, but synthetic images displayed less variation between samples and a narrower range. Mean entropy values were statistically similar (real: 6.97, synthetic: 7.03; p = 0.218), but the range was much narrower for synthetic images (6.91 - 7.11 versus 6.30 - 7.62). When comparing GAN-derived and manually labelled segmentations, intersection-over-union values- denoting the degree of overlap between aponeurosis labels- varied between 0.0280 - 0.612 (mean ± SD: 0.312 ± 0.159), and pennation angles were higher for the GAN-derived segmentations (25.1° vs. 19.3°; p < 0.001). For the second segmentation approach, the algorithm generally performed equally well on synthetic and real images, yielding pennation angles within the physiological range (13.8-20°).

CONCLUSIONS

We used a GAN to generate realistic B-mode ultrasound images, and extracted muscle architectural parameters from these images automatically. This approach could enable generation of large labelled datasets for image segmentation tasks, and may also be useful for data sharing. Automatic generation and labelling of ultrasound images minimises user input and overcomes several limitations associated with manual analysis.

Sarcopenia Detection System Using RGB-D Camera and Ultrasound Probe: System Development and Preclinical In-Vitro Test

Sarcopenia is defined as muscle mass and strength loss with aging. As places, such as South Korea, Japan, and Europe have entered an aged society, sarcopenia is attracting global attention with elderly health. However, only few developed devices can quantify sarcopenia diagnosis modalities. Thus, the authors developed a sarcopenia detection system with 4 degrees of freedom to scan the human thigh with ultrasound probe and determine whether he/she has sarcopenia by inspecting the length of muscle thickness in the thigh by ultrasound image. To accurately measure the muscle thickness, the ultrasound probe attached to the sarcopenia detection system, must be moved angularly along the convex surface of the thigh with predefined pressure maintained. Therefore, the authors proposed an angular thigh scanning method for the aforementioned reason. The method first curve-fits the angular surface of the subject’s thigh with piecewise arcs using D information from a fixed RGB-D camera. Then, it incorporates a Jacobian-based ultrasound probe moving method to move the ultrasound probe along the curve-fitted arc and maintains radial interface force between the probe and the surface by force feedback control. The proposed method was validated by in-vitro test with a human thigh mimicked ham-gelatin phantom. The result showed the ham tissue thickness was maintained within approximately 26.01 ± 1.0 mm during 82° scanning with a 2.5 N radial force setting and the radial force between probe and surface of the phantom was maintained within 2.50 ± 0.1 N.

Automatic segmentation of ultrasound images of gastrocnemius medialis with different echogenicity levels using convolutional neural networks

The purpose of this study was to develop an automatic method for the segmentation of muscle cross-sectional area on transverse B-mode ultrasound images of gastrocnemius medialis using a convolutional neural network(CNN). In the provided dataset images with both normal and increased echogenicity are present. The manually annotated dataset consisted of 591 images, from 200 subjects, 400 relative to subjects with normal echogenicity and 191 to subjects with augmented echogenicity. From the DICOM files, the image has been extracted and processed using the CNN, then the output has been post-processed to obtain a finer segmentation. Final results have been compared to the manual segmentations. Precision and Recall scores as mean ± standard deviation for training, validation, and test sets are 0.96 ± 0.05, 0.90 ± 0.18, 0.89 ± 0.15 and 0.97 ±0.03, 0.89± 0.17, 0.90 ± 0.14 respectively. The CNN approach has also been compared to another automatic algorithm, showing better performances. The proposed automatic method provides an accurate estimation of muscle cross-sectional area in muscles with different echogenicity levels.

Automatic Optic Nerve Measurement: A New Tool to Standardize Optic Nerve Assessment in Ultrasound B-Mode Images

Transorbital sonography provides reliable information about the estimation of intra-cranial pressure by measuring the optic nerve sheath diameter (ONSD), whereas the optic nerve (ON) diameter (OND) may reveal ON atrophy in patients with multiple sclerosis. Here, an AUTomatic Optic Nerve MeAsurement (AUTONoMA) system for OND and ONSD assessment in ultrasound B-mode images based on deformable models is presented. The automated measurements were compared with manual ones obtained by two operators, with no significant differences. AUTONoMA correctly segmented the ON and its sheath in 71 out of 75 images. The mean error compared with the expert operator was 0.06 ± 0.52 mm and 0.06 ± 0.35 mm for the ONSD and OND, respectively. The agreement between operators and AUTONoMA was good and a positive correlation was found between the readers and the algorithm with errors comparable with the inter-operator variability. The AUTONoMA system may allow for standardization of OND and ONSD measurements, reducing manual evaluation variability.

The ultrasonographic measurement of muscle thickness in sarcopenia. A prediction study

BACKGROUND

Sarcopenia is a common disease in the elderly. Although extensive research has been conducted on muscle mass and quality assessment tools, there are still certain drawbacks preventing their universal use.

AIM

The aim of this study was the evaluation of the thickness of head, neck, upper and lower limb muscles measured with ultrasonography, as a potential predictory tool in sarcopenia.

DESIGN

Prediction study.

SETTING

The Outpatient Sarcopenia Clinic of the Rehabilitation Department of the University Hospital of Patras.

POPULATION

Ninety-four individuals (27 men and 67 women) with a mean age of 75.6 years (SD=6.6), referred for sarcopenia screening, participated in this study.

METHODS

The muscle thickness was measured with transverse and longitudinal ultrasound scans bilaterally.

RESULTS

The thickness of the geniohyoid and medial head of gastrocnemius muscle in all ultrasound sections, and the thickness of the rectus femoris and vastus intermedius muscle, in specific sections, was found to be significantly decreased in patients with sarcopenia (P<0.05). The Receiver Operating Characteristic (ROC) curve analysis of the ultrasound muscle thickness measurements resulted in a significant association with sarcopenia. In the case of the geniohyoid muscle, the measured area under the ROC curve was found to be the highest (0.79). The optimal cut-off for the prediction of sarcopenia from the geniohyoid muscle was 0.65 cm with sensitivity equal to 75.0% and specificity equal to 66.7%.

CONCLUSIONS

The results of this study have shown that the thickness of the neck and lower limb muscles measured ultrasonographically can be utilized in the prediction of sarcopenia with high sensitivity and specificity.

CLINICAL REHABILITATION IMPACT

The prevalence of sarcopenia in the geriatric population and the rehabilitation wards is reported to be high. Therefore, an easy, fast, low cost and with no risk, widely available method such as ultrasonography could be an extremely valuable tool for the screening and follow-up of sarcopenia.

Simple Muscle Architecture Analysis (SMA): An ImageJ macro tool to automate measurements in B-mode ultrasound scans

In vivo measurements of muscle architecture (i.e. the spatial arrangement of muscle fascicles) are routinely included in research and clinical settings to monitor muscle structure, function and plasticity. However, in most cases such measurements are performed manually, and more reliable and time-efficient automated methods are either lacking completely, or are inaccessible to those without expertise in image analysis. In this work, we propose an ImageJ script to automate the entire analysis process of muscle architecture in ultrasound images: Simple Muscle Architecture Analysis (SMA). Images are filtered in the spatial and frequency domains with built-in commands and external plugins to highlight aponeuroses and fascicles. Fascicle dominant orientation is then computed in regions of interest using the OrientationJ plugin. Bland-Altman plots of analyses performed manually or with SMA indicate that the automated analysis does not induce any systematic bias and that both methods agree equally through the range of measurements. Our test results illustrate the suitability of SMA to analyse images from superficial muscles acquired with a broad range of ultrasound settings.

Electrical Impedance Methods in Neuromuscular Assessment: An Overview

Electrical impedance methods have been used as evaluation tools in biological and medical science for well over 100 years. However, only recently have these techniques been applied specifically to the evaluation of conditions affecting nerve and muscle. This specific application, termed electrical impedance myography (EIM), is finding wide application as it can provide a quantitative index of muscle condition that can assist with diagnosis, track disease progression, and assess the beneficial impact of therapy. Using noninvasive surface methods, EIM has been studied in a number of conditions ranging from amyotrophic lateral sclerosis to muscular dystrophy to disuse atrophy. Data support that the technique is sensitive to disease status and can offer the possibility of performing clinical trials with fewer subjects than would otherwise be possible. Recent advances in the field include improved approaches for using EIM as a "virtual biopsy" and the development of combined needle impedance-electromyography technology.

Ultrasound-based detection of glucocorticoid-induced impairments of muscle mass and structure in Cushing's disease

PURPOSE

To investigate the glucocorticoid-induced impairments of muscle mass and structure in patients presenting different stages of steroid myopathy progression.

METHODS

Thirty-three patients (28 women) affected by active (N = 20) and remitted (N = 13) Cushing's disease were recruited and the following variables were assessed: walking speed, handgrip strength, total body and appendicular muscle mass by bioelectrical impedance analysis (BIA), thickness and echo intensity of lower limb muscles by ultrasonography.

RESULTS

The two groups of patients showed comparable values of both handgrip strength [median (interquartile range) values: active disease: 27.4 (7.5) kg vs. remitted disease: 26.4 (9.4) kg; P = 0.58] and walking speed [active disease: 1.0 (0.2) m/s vs. remitted disease: 1.1 (0.3) m/s; P = 0.43]. Also, the thickness of the four muscles and all BIA-derived sarcopenic indices were comparable (P > 0.05 for all comparisons) between the two groups. On the contrary, the echo intensity of vastus lateralis, tibialis anterior (lower portion), and medial gastrocnemius was significantly (P < 0.05 for all comparisons) higher in patients with active disease compared to patients with remitted disease. Finally, significant negative correlations were found in the whole group of patients between muscle echo intensity and muscle function assessments.

CONCLUSIONS

We provided preliminary evidence that the ultrasound-derived measurements of muscle thickness and echo intensity can be useful to detect and track the changes of muscle mass and structure in patients with steroid myopathy and we suggest that the combined assessment of muscle mass, strength, and performance should be systematically applied in the routine examination of steroid myopathy patients.

Transverse Muscle Ultrasound Analysis (TRAMA): Robust and Accurate Segmentation of Muscle Cross-Sectional Area

Ultrasonography allows non-invasive and real time-measurement of the visible cross-sectional area (CSA) of muscles, which is a clinically relevant descriptor of muscle size. The aim of this study was to develop and validate a fully automatic method called transverse muscle ultrasound analysis (TRAMA) for segmentation of the muscle in B-mode transverse ultrasound images and measurement of muscle CSA. TRAMA was tested on a database of 200 ultrasound images of the rectus femoris, vastus lateralis, tibialis anterior and medial gastrocnemius muscles. The automatic CSA measurements were compared with manual measurements obtained by two operators. There were no statistical differences between the automatic and manual measurements of CSA of the four muscles, and TRAMA performance was comparable to intra-operator variability in terms of the Dice similarity coefficient and Hausdorff distance between the automatic and manual segmentations. Compared with manual segmentation, the Dice similarity coefficient for the proposed method was always higher than 93%; the Hausdorff distance never exceeded 4 mm, and the maximum absolute error was 62 mm². TRAMA is the first automated algorithm that analyzes and segments ultrasound scans of the muscle in the transverse plane. It can be adopted in future studies for automatic segmentation of muscle regions of interest to enhance and automatize a multitexture analysis of muscle structure.

Relationship between sonography of sternocleidomastoid muscle and cervical passive range of motion in infants with congenital muscular torticollis

Background

An abnormal sternocleidomastoid muscle in congenital muscular torticollis can be classified into one of the four types via sonography. However, this categorization lacks quantitative measurements. The purpose of the study was to determine quantitative measurements of the sonograms via image analysis.

Methods

Infants younger than 12 months of age suspected of having congenital muscular torticollis were included. Intraclass correlation coefficient estimates for interobserver reliability and a simple regression analysis for criterion validity were calculated. Spearman correlation analysis was then performed. The analyzed parameters included cervical passive range of motion for lateral flexion and rotation, area, brightness, max/min Feret's diameters, and muscular width/thickness.

Results

Of the 29 (4.0 ± 2.6 months) screened infants, 13 (1.9 ± 1.7 months) were included. Nine were male, and 4 were female. Seven infants with mass were ultrasonographically classified into type I, and the other six infants were classified into type II. The affected/unaffected side ratios of cervical passive range of motion for lateral flexion and rotation were 0.92 ± 0.13 and 0.88 ± 0.16, respectively. The parameters measured on the sonograms were reliable, and the max/min Feret's diameters were valid measurements. The affected/unaffected side ratio of cervical passive range of motion for rotation significantly correlated with the affected/unaffected side ratios of the sternocleidomastoid muscle sonogram on area (r = −0.62, p = 0.03) and min Feret's diameter (r = −0.69, p = 0.01).

Conclusions

The area and min Feret's diameter were efficacious parameters for image analysis on sternocleidomastoid sonograms, and the min Feret's diameter would be more suitable than thickness for measuring the thickening SCM in transverse view. A healthy control group, more data and follow-up would be needed to confirm the changes on the SCM sonograms for clinical decision.

Lower Limb Motion Estimation Using Ultrasound Imaging: A Framework for Assistive Device Control

OBJECTIVE

Powered assistive devices need improved control intuitiveness to enhance their clinical adoption. Therefore, the intent of individuals should be identified and the device movement should adhere to it. Skeletal muscles contract synergistically to produce defined lower limb movements, so unique contraction patterns in lower extremity musculature may provide a means of device joint control. Ultrasound (US) imaging enables direct measurement of the local deformation of muscle segments. Hence, the objective of this study was to assess the feasibility of using US to estimate human lower limb movements.

METHODS

A novel algorithm was developed to calculate US features of the rectus femoris muscle during a non-weight-bearing knee flexion/extension experiment by nine able-bodied subjects. Five US features of the skeletal muscle tissue were studied, namely thickness, angle between aponeuroses, pennation angle, fascicle length, and echogenicity. A multiscale ridge filter was utilized to extract the structures in the image and a random sample consensus (RANSAC) model was used to segment muscle aponeuroses and fascicles. A localization scheme further guided RANSAC to enable tracking in a US image sequence. Gaussian process regression models were trained using segmented features to estimate both knee joint angle and angular velocity.

RESULTS

The proposed segmentation-estimation approach could estimate knee joint angle and angular velocity with an average root mean square error value of 7.45° and 0.262 rad/s, respectively. The average processing rate was 3-6 frames/s that is promising toward real-time implementation.

CONCLUSION

Experimental results demonstrate the feasibility of using US to estimate human lower extremity motion. The ability of the algorithm to work in real time may enable the use of US as a neural interface for lower limb applications.

SIGNIFICANCE

Intuitive intent recognition of human lower extremity movements using wearable US imaging may enable volitional assistive device control and enhance locomotor outcomes for those with mobility impairments.

Automated localization and segmentation techniques for B-mode ultrasound images: A review

B-mode ultrasound imaging is used extensively in medicine. Hence, there is a need to have efficient segmentation tools to aid in computer-aided diagnosis, image-guided interventions, and therapy. This paper presents a comprehensive review on automated localization and segmentation techniques for B-mode ultrasound images. The paper first describes the general characteristics of B-mode ultrasound images. Then insight on the localization and segmentation of tissues is provided, both in the case in which the organ/tissue localization provides the final segmentation and in the case in which a two-step segmentation process is needed, due to the desired boundaries being too fine to locate from within the entire ultrasound frame. Subsequenly, examples of some main techniques found in literature are shown, including but not limited to shape priors, superpixel and classification, local pixel statistics, active contours, edge-tracking, dynamic programming, and data mining. Ten selected applications (abdomen/kidney, breast, cardiology, thyroid, liver, vascular, musculoskeletal, obstetrics, gynecology, prostate) are then investigated in depth, and the performances of a few specific applications are compared. In conclusion, future perspectives for B-mode based segmentation, such as the integration of RF information, the employment of higher frequency probes when possible, the focus on completely automatic algorithms, and the increase in available data are discussed.

Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods

OBJECTIVE

To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis.

METHODS

Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and "engineered" features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification.

RESULTS

The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C).

CONCLUSIONS

This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification.