Maintenance of a vertically aligned posture of the head and trunk in sitting is a fundamental skill that demonstrates the presence of neuromotor control. Clinical assessments of posture are generally subjective. Studies have quantified posture using different technologies, but the application of such technologies in a clinical environment remains difficult. Video recordings, however, are easily used clinically and have potential for quantitative analysis of movement. This study used a video-based method to generate a numerical measure of postural alignment of the head and trunk in sitting. Static and dynamic trials of 12 healthy seated adults were simultaneously recorded with a sagittal video camera and a 3D motion capture system. Segmental angles were calculated for the Head, Neck and six Trunk segments. An agreed definition of aligned static sitting posture agreed was used by five clinically experienced experts to identify video frames where the participants' posture was aligned. The five subsets of frames that defined the aligned posture were combined to give aligned segments (mean±SD) for each participant. Agreement between experts in the definition (mean) of aligned segmental angles was excellent (ICC=0.99) and intra-assessor reliability (SD) lay within 2.1°-11.6°. Agreement between the video-based method and the 3D system was below 3.8° and 8.4° for static and dynamic trials respectively. This video-based method allowed the quantification of sitting posture and provided greater detail of the trunk/spinal profile than previous methods. It has potential as a complementary tool, alongside subjective assessments, for patients with a wide variety of pathologies.
Objectives: To automate online segmentation of cervical muscles from transverse ultrasound (US) images of the human neck during functional head movement. To extend ground-truth labelling methodology beyond dependence upon MRI imaging of static head positions required for application to participants with involuntary movement disorders. Method: We collected sustained sequences (> 3 minutes) of US images of human posterior cervical neck muscles at 25 fps from 28 healthy adults, performing visually-guided pitch and yaw head motions. We sampled 1,100 frames (approx. 40 per participant) spanning the experimental range of head motion. We manually labelled all 1,100 US images and trained deconvolutional neural networks (DCNN) with a spatial SoftMax regression layer to classify every pixel in the full resolution (525x491) US images, as one of 14 classes (10 muscles, ligamentum nuchae, vertebra, skin, background). We investigated ‘MaxOut’ and Exponential Linear unit (ELU) transfer functions and compared with our previous benchmark (analytical shape modelling). Results: These DCNNs showed higher Jaccard Index (53.2%) and lower Hausdorff Distance (5.7 mm) than the previous benchmark (40.5%, 6.2 mm). SoftMax Confidence corresponded with correct classification. ‘MaxOut’ outperformed ELU marginally. Conclusion: The DCNN architecture accommodates challenging images and imperfect manual labels. The SoftMax layer gives user feedback of likely correct classification. The ‘MaxOut’ transfer function benefits from near-linear operation, compatibility with deconvolution operations and the dropout regulariser. Significance: This methodology for labelling ground-truth and training automated labelling networks is applicable for dynamic segmentation of moving muscles and for participants with involuntary movement disorders who cannot remain still.
Highlights This study developed an objective measure of head/trunk control in children with CP Simplified objective rules of an existing clinical test (SATCo) were generated These objective rules successfully mirrored the subjective assessment The SATCo is supported and validated by objective correlates The results give support to future full automation of trunk control measurement
This paper presents an investigation into the feasibility of using deep learning methods for developing arbitrary full spatial resolution regression analysis of B-mode ultrasound images of human skeletal muscle. In this study, we focus on full spatial analysis of muscle fibre orientation, since there is an existing body of work with which to compare results. Previous attempts to automatically estimate fibre orientation from ultrasound are not adequate, often requiring manual region selection, feature engineering, providing low-resolution estimations (one angle per muscle) and deep muscles are often not attempted. We build upon our previous work in which automatic segmentation was used with plain convolutional neural network (CNN) and deep residual convolutional network (ResNet) architectures, to predict a low-resolution map of fibre orientation in extracted muscle regions. Here, we use deconvolutions and max-unpooling (DCNN) to regularise and improve predicted fibre orientation maps for the entire image, including deep muscles, removing the need for automatic segmentation and we compare our results with the CNN and ResNet, as well as a previously established feature engineering method, on the same task. Dynamic ultrasound images sequences of the calf muscles were acquired (25 Hz) from 8 healthy volunteers (4 male, ages: 25-36, median 30). A combination of expert annotation and interpolation/extrapolation provided labels of regional fibre orientation for each image. Neural networks (CNN, ResNet, DCNN) were then trained both with and without dropout using leave one out cross-validation. Our results demonstrated robust estimation of full spatial fibre orientation within approximately 6 • error, which was an improvement on previous methods.
There has been a general consensus in recent literature that internationalization is a key success factor in the competitive business environment we now live in. Using a sample of Spanish agri‐food companies, the main objective of this research is to determine the linkage of Management Control Systems (MCS), ISO certifications, internationalization and performance. The results obtained reveal, as expected, that internationalization has a direct and positive relationship with organizational performance. In addition, we find that both the use of MCS and the deployment of ISO certification positively influence companies' level of internationalization. Nevertheless, our findings clearly indicate that MCS and ISO do not influence performance directly.
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