Abstract-Manual and automated segmentation of individual muscles in magnetic resonance images have been recognized as challenging given the high variability of shapes between muscles and subjects and the discontinuity or lack of visible boundaries between muscles. In the present study, we proposed an original algorithm allowing a semi-automatic transversal propagation of manually-drawn masks. Our strategy was based on several ascending and descending non-linear registration approaches which is similar to the estimation of a Lagrangian trajectory applied to manual masks. Using several manually-segmented slices, we have evaluated our algorithm on the four muscles of the quadriceps femoris group. We mainly showed that our 3D propagated segmentation was very accurate with an averaged Dice similarity coefficient value higher than 0.91 for the minimal manual input of only two manually-segmented slices.

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.

PurposeTo propose a novel segmentation framework that is dedicated to the follow‐up of fat infiltration in individual muscles of patients with neuromuscular disorders.MethodsWe designed a semi‐automatic segmentation pipeline of individual leg muscles in MR images based on automatic propagation through nonlinear registrations of initial delineation in a minimal number of MR slices. This approach has been validated for the segmentation of individual muscles from MRI data sets, acquired over a 10‐month period, from thighs and legs in 10 patients with muscular dystrophy. The robustness of the framework was evaluated using conventional metrics related to muscle volume and clinical metrics related to fat infiltration.ResultsHigh accuracy of the semi‐automatic segmentation (mean Dice similarity coefficient higher than 0.89) was reported. The provided method has excellent reliability regarding the reproducibility of the fat fraction estimation, with an average intraclass correlation coefficient score of 0.99. Furthermore, the present segmentation framework was determined to be more reliable than the intra‐expert performance, which had an average intraclass correlation coefficient of 0.93.ConclusionThe proposed framework of segmentation can successfully provide an effective and reliable tool for accurate follow‐up of any MRI biomarkers in neuromuscular disorders. This method could assist the quantitative assessment of muscular changes occurring in such diseases.

Purpose To demonstrate the reproducibility of the diffusion properties and three-dimensional structural organization measurements of the lower leg muscles by using diffusion-tensor imaging (DTI) assessed with ultra-high-field-strength (7.0-T) magnetic resonance (MR) imaging and tractography of skeletal muscle fibers. On the basis of robust statistical mapping analyses, this study also aimed at determining the sensitivity of the measurements to sex difference and intramuscular variability. Materials and Methods All examinations were performed with ethical review board approval; written informed consent was obtained from all volunteers. Reproducibility of diffusion tensor indexes assessment including eigenvalues, mean diffusivity, and fractional anisotropy (FA) as well as muscle volume and architecture (ie, fiber length and pennation angle) were characterized in lower leg muscles (n = 8). Intramuscular variability and sex differences were characterized in young healthy men and women (n = 10 in each group). Student t test, statistical parametric mapping, correlation coefficients (Spearman rho and Pearson product-moment) and coefficient of variation (CV) were used for statistical data analysis. Results High reproducibility of measurements (mean CV ± standard deviation, 4.6% ± 3.8) was determined in diffusion properties and architectural parameters. Significant sex differences were detected in FA (4.2% in women for the entire lower leg; P = .001) and muscle volume (21.7% in men for the entire lower leg; P = .008), whereas architecture parameters were almost identical across sex. Additional differences were found independently of sex in diffusion properties and architecture along several muscles of the lower leg. Conclusion The high-spatial-resolution DTI assessed with 7.0-T MR imaging allows a reproducible assessment of structural organization of superficial and deep muscles, giving indirect information on muscle function. RSNA, 2018 Online supplemental material is available for this article.

ObjectiveTo quantitatively describe the MRI fat infiltration pattern of muscle degeneration in Charcot-Marie-Tooth (CMT) type 1A (CMT1A) disease and to look for correlations with clinical variables.MethodsMRI fat fraction was assessed in lower-limb musculature of patients with CMT1A and healthy controls. More particularly, 14 muscle compartments were selected at leg and thigh levels and for proximal, distal, and medial slices. Muscle fat infiltration profile was determined quantitatively in each muscle compartment and along the entire volume of acquisition to determine a length-dependent gradient of fat infiltration. Clinical impairment was evaluated with muscle strength measurements and CMT Examination Scores (CMTESs).ResultsA total of 16 patients with CMT1A were enrolled and compared to 11 healthy controls. Patients with CMT1A showed a larger muscle fat fraction at leg and thigh levels with a proximal-to-distal gradient. At the leg level, the largest fat infiltration was quantified in the anterior and lateral compartments. CMTES was correlated with fat fraction, especially in the anterior compartment of leg muscles. Strength of plantar flexion was also correlated with fat fraction of the posterior compartments of leg muscles.ConclusionOn the basis of quantitative MRI measurements combined with a dedicated segmentation method, muscle fat infiltration quantified in patients with CMT1A disclosed a length-dependent peroneal-type pattern of fat infiltration and was correlated to main clinical variables. Quantification of fat fraction at different levels of the leg anterior compartment might be of interest in future clinical trials.

Background Fat infiltration in individual muscles of sporadic inclusion body myositis (sIBM) patients has rarely been assessed. Methods Sixteen sIBM patients were assessed using MRI of the thighs and lower legs (LL). The severity of fat infiltration, proximal‐to‐distal and side asymmetries, and the correlations with clinical and functional parameters were investigated. Results All the patients had fat‐infiltrated muscles, and thighs were more severely affected than LL. A proximal‐to‐distal gradient of fat infiltration was mainly observed for adductors, quadriceps, sartorius, and medial gastrocnemius muscles. A strong negative correlation was observed between the whole muscle fat fraction in the thighs and LL and the Inclusion Body Myositis Functional Rating Scale and Medical Research Council scores for the lower limbs. Conclusions Fat infiltration in individual muscles of sIBM patients is heterogeneous in terms of proximal‐to‐distal gradient and severity was correlated with clinical scores. These results should be considered for both natural history investigation and clinical trials.

The objective of this study is to develop, test and validate a fully automatic, deep learning-based segmentation method for the wrist joint cartilage in magnetic resonance images. The study was conducted in 8 healthy volunteers and 3 patients with wrist joint diseases. 3D MRI datasets (20 in total) were acquired at 1.5T using a VIBE sequence. Wrist cartilage was segmented on coronal slices by a clinician and the convolutional neural network (CNN) was trained, developed and tested using the corresponding segmented masks. For an inter and intra observer study wrist cartilage was segmented by three observers once and twice by one observer on a dataset of 20 central coronal slices. Performance of the CNN was compared quantitatively to the manual segmentations using the concordance and the Sørensen-Dice similarity coefficients (DSC). Cartilage segmentations obtained with the CNN showed a substantial agreement with the manual segmentations for the whole wrist joint (DSC = 0.73) and a good agreement (DSC = 0.81) for the central coronal slices. The inter-and intra-observer concordance indices for manual segmentations were 0.55 and 0.85, respectively. The concordance index of the CNN-based segmentation was 0.69 when compared to the manual segmentations. The fully automatic deep-learning based segmentation of the wrist cartilage showed a high concordance with the manual measurements. It could be applied to determine an automatic, quantitative metric in clinical wrist cartilage studies.

Key points T2 mapping combined to image registration and statistical parametric mapping analysis is a suitable methodology to accurately localize and compare the extent of both activated and damaged muscle areas. Activated muscle areas following electrically‐induced isometric contractions are superficial, but damaged regions are muscle specific and can be related to the muscle morphology and/or the relative spatial position within a muscle group leading to potential intramuscular muscle shear strain. Tissues other than active skeletal muscle fibres can be altered during unaccustomed neuromuscular electrical stimulation‐induced isometric contractions. Abstract Skeletal muscle isometric contractions induced by neuromuscular electrical stimulation (NMES) exercise can generate damage within activated muscles. This study aimed at comparing the localization and the extent of NMES‐activated muscle areas and induced damage regions using magnetic resonance imaging. Thirteen healthy subjects performed a single bout of NMES‐induced isometric contractions known to induce a decrease in maximal voluntary isometric contraction (MVC) and increase in muscle volume and transverse relaxation time (T2). All the parameters were measured before, immediately after (POST), 7 days (D7), 14 days (D14) and 21 days (D21) after the NMES session. Spatial normalization of T2 maps were performed to compare the localization of muscle activation areas and damaged muscle regions from statistical mapping analyses. A significant decrease in MVC was found at POST (−26 ± 9%) and in delayed time at D7 (−20 ± 6%) and D14 (−12 ± 5%). Although muscle activation was statistically detected through T2 increase at POST in superficial parts of the two muscles located beneath the stimulation electrodes (i.e. vastus lateralis and vastus medialis), alterations quantified in a delayed time from increased T2 were mainly located in the deep muscle region of the vastus lateralis (+57 ± 24% of mean T2) and superficial area of the vastus medialis (+24 ± 16% of mean T2) at D7 and were still observed in whole muscle at D21. The discrepancy between activated and damaged areas in the vastus lateralis implies that tissues other than active skeletal muscle fibres were altered during unaccustomed NMES‐induced isomeric contractions.