Automated multi-atlas segmentation of gluteus maximus from Dixon and T1-weighted magnetic resonance images

Gender similarities and differences in skeletal muscle and body composition: an MRI study of recreational cyclists

Objectives

This study aims to quantitatively evaluate whether there are muscle mass differences between male and female recreational cyclists and compare muscle quality and body composition in the pelvis region between two well-matched groups of fit and healthy male and female adults.

Methods

This cross-sectional study involved 45 female and 42 male recreational cyclists. The inclusion criteria for both groups were to have cycled more than 7000 km in the last year, have an absence of injuries and other health problems, have no contraindication to MRI, and be 30-65 years old. Our main outcome measures were fat fraction, as a measure of intramuscular fat (IMF) content, and volume of the gluteal muscles measured using Dixon MRI. The gluteal subcutaneous adipose tissue (SAT) volume was evaluated as a secondary measure.

Results

We found that there were no gender differences in the IMF content of gluteus maximus (GMAX, p=0.42), gluteus medius (GMED, p=0.69) and gluteus minimus (GMIN, p=0.06) muscles, despite women having more gluteal SAT (p<0.01). Men had larger gluteal muscles than women (p<0.01), but no differences were found when muscle volume was normalised by body weight (GMAX, p=0.54; GMED, p=0.14; GMIN, p=0.19).

Conclusions

Our study shows that despite the recognised hormonal differences between men and women, there is gender equivalence in the muscle mass and quality of the gluteal muscles when matched for exercise and body weight. This new MRI study provides key information to better understand gender similarities and differences in skeletal muscle and body composition.

Mid-life cyclists preserve muscle mass and composition: a 3D MRI study

Physical activity and a healthy lifestyle are crucial factors for delaying and reducing the effects of sarcopenia. Cycling has gained popularity in the last decades among midlife men. While the cardiovascular benefits of cycling and other endurance exercises have been extensively proved, the potential benefits of lifelong aerobic exercise on muscle health have not been adequately studied. Our aim was to quantify the benefits of cycling in terms of muscle health in middle-aged men, using magnetic resonance imaging. We ran a cross-sectional study involving two groups of middle-aged male adults (mean age 49 years, range 30-65) that underwent Dixon MRI of the pelvis. The groups consisted of 28 physically inactive (PI) and 28 trained recreational cyclists. The latter had cycled more than 7000 km in the last year and have been training for 15 years on average, while the PI volunteers have not practiced sports for an average of 27 years. We processed the Dixon MRI scans by labelling and computing the fat fraction (FF), volume and lean volume of gluteus maximus (GMAX) and gluteus medius (GMED); and measuring the volume of subcutaneous adipose tissue (SAT). We found that the cyclists group had lower FF levels, a measure of intramuscular fat infiltration, compared to the PI group for GMAX (PI median FF 21.6%, cyclists median FF 14.8%, p < 0.01) and GMED (PI median FF 16.0%, cyclists median FF 11.4%, p < 0.01). Cyclists had also larger GMAX and GMED muscles than the PI group (p < 0.01), after normalizing it by body mass. Muscle mass and fat infiltration were strongly correlated with SAT volume. These results suggest that cycling could help preserve muscle mass and composition in middle-aged men. Although more research is needed to support these results, this study adds new evidence to support public health efforts to promote cycling.

Towards defining muscular regions of interest from axial magnetic resonance imaging with anatomical cross-reference: a scoping review of lateral hip musculature

Background

Measures of hip muscle morphology and composition (e.g., muscle size and fatty infiltration) are possible with magnetic resonance imaging (MRI). Standardised protocols or guidelines do not exist for evaluation of hip muscle characteristics, hindering reliable and valid inter-study analysis. This scoping review aimed to collate and synthesise MRI methods for measuring lateral hip muscle size and fatty infiltration to inform the future development of standardised protocols.

Methods

Five electronic databases (Medline, CINAHL, Embase, SportsDISCUS and AMED) were searched. Healthy or musculoskeletal pain populations that used MRI to assess lateral hip muscle size and fatty infiltration were included. Lateral hip muscles of interest included tensor fascia late (TFL), gluteus maximus, gluteus medius, and gluteus minimus. Data on MRI parameters, axial slice location, muscle size and fatty infiltrate measures were collected and analysed. Cross referencing for anatomical locations were made between MRI axial slice and E-12 anatomical plastinate sections.

Results

From 2684 identified publications, 78 studies contributed data on volume (n = 31), cross sectional area (CSA) (n = 24), and fatty infiltration (n = 40). Heterogeneity was observed for MRI parameters and anatomical boundaries scrutinizing hip muscle size and fatty infiltration. Seven single level axial slices were identified that provided consistent CSA measurement, including three for both gluteus maximus and TFL, and four for both gluteus medius and minimus. For assessment of fatty infiltration, six axial slice locations were identified including two for TFL, and four for each of the gluteal muscles.

Conclusions

Several consistent anatomical levels were identified for single axial MR slice to facilitate muscle size and fatty infiltration muscle measures at the hip, providing the basis for reliable and accurate data synthesis and improvements in the validity of future between studies analyses. This work establishes the platform for standardised methods for the MRI assessment of lateral hip musculature and will aid in the examination of musculoskeletal conditions around the hip joint. Further studies into whole muscle measures are required to further optimise methodological parameters for hip muscle assessment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05439-x.

Design of a Classification Recognition Model for Bone and Muscle Anatomical Imaging Based on Convolutional Neural Network and 3D Magnetic Resonance

In this paper, we use convolutional neural networks to conduct in-depth research and analysis on the classification and recognition of bone and muscle anatomical imaging graphics of 3D magnetic resonance and design corresponding models for practical applications. A series of medical image segmentation models based on convolutional neural networks is proposed. In this paper, firstly, a separated attention mechanism is introduced in the model, which divides the input data into multiple paths, applies self-attention weights to adjacent data paths, and finally fuses the weighted values to form the basic convolutional block. This structure has multiple parallel data paths, which increases the width of the network and therefore improves the feature extraction capability of the model. Then, this paper proposes a bidirectional feature pyramid for medical image segmentation task, which has top-down and bottom-up data paths, and, together with jump connections, can fully interact with feature maps at different scales. After that, a new activation function Mish is introduced, and its advantages over other activation functions are experimentally demonstrated. Finally, for the situation that medical image annotations are not easy to obtain, a semisupervised learning method is introduced in the model training process, and the effectiveness of this method is experimentally demonstrated. The joint network first denoises the input image, then super-resolution mapping is performed on the noise-removed feature map, and finally, the super-resolution 3D-MR image is obtained. We update the network by combining the denoising loss and super-resolution loss during the joint network training process. The experimental results show that the joint network with denoising first and then super-resolution outperforms the joint network with other task order and outperforms the method that performs the two tasks separately and the proposed method in this paper has the optimal performance.

Reference values for volume, fat content and shape of the hip abductor muscles in healthy individuals from Dixon MRI

Healthy hip abductor muscles are a good indicator of a healthy hip and an active lifestyle, as they are greatly involved in human daily activities. Fatty infiltration and muscle atrophy are associated with loss of strength, loss of mobility and hip disease. However, these variables have not been widely studied in this muscle group. We aimed to characterize the hip abductor muscles in a group of healthy individuals to establish reference values for volume, intramuscular fat content and shape of this muscle group. To achieve this, we executed a cross-sectional study using Dixon MRI scans of 51 healthy subjects. We used an automated segmentation method to label GMAX, GMED, GMIN and TFL muscles, measured normalized volume (NV) using lean body mass, fat fraction (FF) and lean muscle volume for each subject and computed non-parametric statistics for each variable grouped by sex and age. We measured these variables for each axial slice and created cross-sectional area and FF axial profiles for each muscle. Finally, we generated sex-specific atlases with FF statistical images. We measured median (IQR) NV values of 12.6 (10.8-13.8), 6.3 (5.6-6.7), 1.6 (1.4-1.7) and 0.8 (0.6-1.0) cm³ /kg for GMAX, GMED, GMIN and TFL, and median (IQR) FF values of 12.3 (10.1-15.9)%, 9.8 (8.6-11.2)%, 10.0 (9.0-12.0)% and 10.2 (7.8-13.5)% respectively. FF values were significantly higher for females for the four muscles (p < 0.01), but there were no significant differences between the two age groups. When comparing individual muscles, we observed a significantly higher FF in GMAX than in the other muscles. The reported novel reference values and axial profiles for volume and FF of the hip abductors, together with male and female atlases, are tools that could potentially help to quantify and detect early the deteriorating effects of hip disease or sarcopenia.

Intramuscular fat in gluteus maximus for different levels of physical activity

We aimed to determine if gluteus maximus (GMAX) fat infiltration is associated with different levels of physical activity. Identifying and quantifying differences in the intramuscular fat content of GMAX in subjects with different levels of physical activity can provide a new tool to evaluate hip muscles health. This was a cross-sectional study involving seventy subjects that underwent Dixon MRI of the pelvis. The individuals were divided into four groups by levels of physical activity, from low to high: inactive patients due to hip pain; and low, medium and high physical activity groups of healthy subjects (HS) based on hours of exercise per week. We estimated the GMAX intramuscular fat content for each subject using automated measurements of fat fraction (FF) from Dixon images. The GMAX volume and lean volume were also measured and normalized by lean body mass. The effects of body mass index (BMI) and age were included in the statistical analysis. The patient group had a significantly higher FF than the three groups of HS (median values of 26.2%, 17.8%, 16.7% and 13.7% respectively, p < 0.001). The normalized lean volume was significantly larger in the high activity group compared to all the other groups (p < 0.001, p = 0.002 and p = 0.02). Employing a hierarchical linear regression analysis, we found that hip pain, low physical activity, female gender and high BMI were statistically significant predictors of increased GMAX fat infiltration.

Overview of MR Image Segmentation Strategies in Neuromuscular Disorders

Neuromuscular disorders are rare diseases for which few therapeutic strategies currently exist. Assessment of therapeutic strategies efficiency is limited by the lack of biomarkers sensitive to the slow progression of neuromuscular diseases (NMD). Magnetic resonance imaging (MRI) has emerged as a tool of choice for the development of qualitative scores for the study of NMD. The recent emergence of quantitative MRI has enabled to provide quantitative biomarkers more sensitive to the evaluation of pathological changes in muscle tissue. However, in order to extract these biomarkers from specific regions of interest, muscle segmentation is mandatory. The time-consuming aspect of manual segmentation has limited the evaluation of these biomarkers on large cohorts. In recent years, several methods have been proposed to make the segmentation step automatic or semi-automatic. The purpose of this study was to review these methods and discuss their reliability, reproducibility, and limitations in the context of NMD. A particular attention has been paid to recent deep learning methods, as they have emerged as an effective method of image segmentation in many other clinical contexts.

Use of artificial intelligence in the imaging of sarcopenia: A narrative review of current status and perspectives

Sarcopenia is a muscle disease which previously was associated only with aging, but in recent days it has been gaining more attention for its predictive value in a vast range of conditions and its potential link with overall health. Up to this point, evaluating sarcopenia with imaging methods has been time-consuming and dependent on the skills of the physician. The solution for this problem may be found in artificial intelligence, which may assist radiologists in repetitive tasks such as muscle segmentation and body-composition analysis. The major aim of this review was to find and present the current status and future perspectives of artificial intelligence in the imaging of sarcopenia. We searched the PubMed database to find articles concerning the use of artificial intelligence in diagnostic imaging and especially in body-composition analysis in the context of sarcopenia. We found that artificial-intelligence systems could potentially help with evaluating sarcopenia and better predicting outcomes in a vast range of clinical situations, which could get us closer to the true era of precision medicine.