Automatic segmentation of skeletal muscles from MR images using modified U-Net and a novel data augmentation approach

Rapid and accurate muscle segmentation is essential for the diagnosis and monitoring of many musculoskeletal diseases. As gold standard, manual annotation suffers from intensive labor and high inter-operator reproducibility errors. In this study, deep learning (DL) based automatic muscle segmentation from MR scans is investigated for post-menopausal women, who normally experience a decline in muscle volume. The performance of four Deep Learning (DL) models was evaluated: U-Net and UNet++ and two modified U-Net networks, which combined feature fusion and attention mechanisms (Feature-Fusion-UNet, FFU, and Attention-Feature-Fusion-UNet, AFFU). The models were tested for automatic segmentation of 16-lower limb muscles from MRI scans of two cohorts of post-menopausal women (11 subjects in PMW-1, 8 subjects in PMW-2; from two different studies so considered independent datasets) and 10 obese post-menopausal women (PMW-OB). Furthermore, a novel data augmentation approach is proposed to enlarge the training dataset. The results were assessed and compared by using the Dice similarity coefficient (DSC), relative volume error (RVE), and Hausdorff distance (HD). The best performance among all four DL models was achieved by AFFU (PMW-1: DSC 0.828 ± 0.079, 1-RVE 0.859 ± 0.122, HD 29.9 mm ± 26.5 mm; PMW-2: DSC 0.833 ± 0.065, 1-RVE 0.873 ± 0.105, HD 25.9 mm ± 27.9 mm; PMW-OB: DSC 0.862 ± 0.048, 1-RVE 0.919 ± 0.076, HD 34.8 mm ± 46.8 mm). Furthermore, the augmentation of data significantly improved the DSC scores of U-Net and AFFU for all 16 tested muscles (between 0.23% and 2.17% (DSC), 1.6%–1.93% (1-RVE), and 9.6%–19.8% (HD) improvement). These findings highlight the feasibility of utilizing DL models for automatic segmentation of muscles in post-menopausal women and indicate that the proposed augmentation method can enhance the performance of models trained on small datasets

Mapping and Analyzing Stakeholders in China’s Essential Drug System by Using a Circular Model: Who We Should Deal with Next?

AbstractObjectivesTo predict the prospects of the essential drug system by using the Stakeholder Impact Index (SII) and evaluate the current performance of each main stakeholder and suggested dangerous stakeholders and dormant stakeholders.MethodsA Delphi method was used, involving 36 experts with experience in implementation and evaluation of the essential drug policy, to construct the circular model as well as evaluate the performance of each stakeholder.ResultsThe central government was a dominant stakeholder of the whole essential drug system. The provincial governments were definitive stakeholders, whereas local governments and medical institutions were dependent stakeholders. Furthermore, media and drug stores were dormant stakeholders and pharmaceutical manufacturers and delivery enterprises were dangerous stakeholders. Patients, community residents, and medical insurance programs were discretionary stakeholders. The SII for the essential drug system was positive (SIIproj⁎ = 2.72).ConclusionsThe overall anticipation of the essential drug policy is optimistic. Letting definitive stakeholders (provincial governments) having more autonomy can efficiently accelerate the pace of implementation of the essential drug policy in the current situation. Central government, however, also needs to construct an experience exchange platform with the aim of building versatile methods for running the essential drug system in all provinces. Pharmaceutical manufacturers and delivery enterprises were dangerous stakeholders for the essential drug policy. Because of their potential threat to the implementation of the policy, the central government should motivate them to support the construction of the essential drug system spontaneously. In that case, provincial governments need to construct a fair, balanced, and self-stabilized bidding platform

An extended discrete element method for the estimation of contact pressure at the ankle joint during stance phase

Abnormalities in the ankle contact pressure are related to the onset of osteoarthritis. In vivo measurements are not possible with currently available techniques, so computational methods such as the finite element analysis (FEA) are often used instead. The discrete element method (DEM), a computationally efficient alternative to time-consuming FEA, has also been used to predict the joint contact pressure. It describes the articular cartilage as a bed of independent springs, assuming a linearly elastic behaviour and absence of relative motion between the bones. In this study, we present the extended DEM (EDEM) which is able to track the motion of talus over time. The method was used, with input data from a subject-specific musculoskeletal model, to predict the contact pressure in the ankle joint during gait. Results from EDEM were also compared with outputs from conventional DEM. Predicted values of contact area were larger in EDEM than they were in DEM (4.67 and 4.18 cm2, respectively). Peak values of contact pressure, attained at the toe-off, were 7.3 MPa for EDEM and 6.92 MPa for DEM. Values predicted from EDEM fell well within the ranges reported in the literature. Overall, the motion of the talus had more effect on the extension and shape of the pressure distribution than it had on the magnitude of the pressure. The results indicated that EDEM is a valid methodology for the prediction of ankle contact pressure during daily activities

Applications of neural networks in nonlinear dynamic systems

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Stable adaptive neurocontrol for nonlinear discrete-time systems

This paper presents a novel approach in designing neural network based adaptive controllers for a class of nonlinear discrete-time systems. This type of controllers has its simplicity in parallelism to linear generalized minimum variance (GMV) controller design and efficiency to deal with complex nonlinear dynamics. A recurrent neural network is introduced as a bridge to compensation simplify controller design procedure and efficiently to deal with nonlinearity. The network weight adaptation law is derived from Lyapunov stability analysis and the connection between convergence of the network weight and the reconstruction error of the network is established. A theorem is presented for the conditions of the stability of the closed-loop systems. Two simulation examples are provided to demonstrate the efficiency of the approach

Data-Driven Based Modelling of Pressure Dynamics in Multiphase Reservoir Model

Secondary recovery involves injecting water or gas into reservoirs to maintain or boost the pressure and sustain production levels at viable rates. Accurate tracking of pressure dynamics as reservoirs produce under secondary production is one of the challenging tasks in reservoir modelling. In this paper, a data-driven based technique called Dynamic Mode Learning (DML) that aims to provide an efficient alternative approach for learning and decomposing pressure dynamics in multiphase reservoir model that produces under secondary recovery is proposed. Existing algorithms suffer from complexity and thereby resulting to expensive computational demand. The proposed DML technique is developed in the form of a learning system by first, constructing a simple, fast and efficient learning system that extracts important features from original full-state data and places them in a low-dimensional representation as extracted features. The extracted features are then used to reduce the original high-dimensional data after which dynamic modes are computed on the reduced data. The performance of the proposed DML method is illustrated on pressure field data generated from direct numerical simulations. Experimental results performed on the reference data reveal that the proposed DML method exhibits better and effective performance over standard and compressed dynamic mode decomposition (DMD) mainstream algorithms

Effects of Dexmedetomidine on motor- and somatosensory-evoked potentials in patients with thoracic spinal cord tumor: a randomized controlled trial.

BackgroundWe hypothesized that the addition of dexmedetomidine in a clinically relevant dose to propofol-remifentanil anesthesia regimen does not exert an adverse effect on motor-evoked potentials (MEP) and somatosensory-evoked potentials (SSEP) in adult patients undergoing thoracic spinal cord tumor resection.MethodsSeventy-one adult patients were randomized into three groups. Propofol group (n = 25): propofol-remifentanil regimenand the dosage was adjusted to maintain the bispectral index (BIS) between 40 and 50. DP adjusted group (n = 23): Dexmedetomidine (0.5 μg/kg loading dose infused over 10 min followed by a constant infusion of 0.5 μg/kg/h) was added to the propofol-remifentanil regimen and propofol was adjusted to maintain BIS between 40 and 50. DP unadjusted group (n = 23): Dexmedetomidine (administer as DP adjusted group) was added to the propofol-remifentanil regimen and propofol was not adjusted. All patients received MEP, SSEP and BIS monitoring.ResultsThere were no significant changes in the amplitude and latency of MEP and SSEP among different groups (P > 0.05). The estimated propofol plasma concentration in DP adjusted group (2.7 ± 0.3 μg/ml) was significantly lower than in propofol group (3.1 ± 0.2 μg/ml) and DP unadjusted group (3.1 ± 0.2 μg/ml) (P = 0.000). BIS in DP unadjusted group (35 ± 5) was significantly lower than in propofol group (44 ± 3) (P = 0.000).ConclusionsThe addition of dexmedetomidine to propofol-remifentanil regimen does not exert an adverse effect on MEP and SSEP monitoring in adult patients undergoing thoracic spinal cord tumor resection.Trial registrationThe study was registered with the Chinese Clinical Trial Registry on January 31st, 2014. The reference number was ChiCTR-TRC-14004229

Table1_Automatic segmentation of skeletal muscles from MR images using modified U-Net and a novel data augmentation approach.docx

Rapid and accurate muscle segmentation is essential for the diagnosis and monitoring of many musculoskeletal diseases. As gold standard, manual annotation suffers from intensive labor and high inter-operator reproducibility errors. In this study, deep learning (DL) based automatic muscle segmentation from MR scans is investigated for post-menopausal women, who normally experience a decline in muscle volume. The performance of four Deep Learning (DL) models was evaluated: U-Net and UNet++ and two modified U-Net networks, which combined feature fusion and attention mechanisms (Feature-Fusion-UNet, FFU, and Attention-Feature-Fusion-UNet, AFFU). The models were tested for automatic segmentation of 16-lower limb muscles from MRI scans of two cohorts of post-menopausal women (11 subjects in PMW-1, 8 subjects in PMW-2; from two different studies so considered independent datasets) and 10 obese post-menopausal women (PMW-OB). Furthermore, a novel data augmentation approach is proposed to enlarge the training dataset. The results were assessed and compared by using the Dice similarity coefficient (DSC), relative volume error (RVE), and Hausdorff distance (HD). The best performance among all four DL models was achieved by AFFU (PMW-1: DSC 0.828 ± 0.079, 1-RVE 0.859 ± 0.122, HD 29.9 mm ± 26.5 mm; PMW-2: DSC 0.833 ± 0.065, 1-RVE 0.873 ± 0.105, HD 25.9 mm ± 27.9 mm; PMW-OB: DSC 0.862 ± 0.048, 1-RVE 0.919 ± 0.076, HD 34.8 mm ± 46.8 mm). Furthermore, the augmentation of data significantly improved the DSC scores of U-Net and AFFU for all 16 tested muscles (between 0.23% and 2.17% (DSC), 1.6%–1.93% (1-RVE), and 9.6%–19.8% (HD) improvement). These findings highlight the feasibility of utilizing DL models for automatic segmentation of muscles in post-menopausal women and indicate that the proposed augmentation method can enhance the performance of models trained on small datasets.</p