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Estimation of Absolute States of Human Skeletal Muscle via Standard B-Mode Ultrasound Imaging and Deep Convolutional Neural Networks
Objective: To test automated in vivo estimation of active and passive sk...
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Fully automated analysis of muscle architecture from B-mode ultrasound images with deep learning
B-mode ultrasound is commonly used to image musculoskeletal tissues, but...
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Localization of Fetal Head in Ultrasound Images by Multiscale View and Deep Neural Networks
One of the routine examinations that are used for prenatal care in many ...
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Investigation and Assessment of Disorder of Ultrasound B-mode Images
Digital image plays a vital role in the early detection of cancers, such...
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Quantification of Transducer Misalignment in Ultrasound Tongue Imaging
In speech production research, different imaging modalities have been em...
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Delineating Bone Surfaces in B-Mode Images Constrained by Physics of Ultrasound Propagation
Bone surface delineation in ultrasound is of interest due to its potenti...
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Deep learning with spatiotemporal consistency for nerve segmentation in ultrasound images
Ultrasound-Guided Regional Anesthesia (UGRA) has been gaining importance...
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The application of deep convolutional neural networks to ultrasound for modelling of dynamic states within human skeletal muscle
This paper concerns the fully automatic direct in vivo measurement of active and passive dynamic skeletal muscle states using ultrasound imaging. Despite the long standing medical need (myopathies, neuropathies, pain, injury, ageing), currently technology (electromyography, dynamometry, shear wave imaging) provides no general, non-invasive method for online estimation of skeletal intramuscular states. Ultrasound provides a technology in which static and dynamic muscle states can be observed non-invasively, yet current computational image understanding approaches are inadequate. We propose a new approach in which deep learning methods are used for understanding the content of ultrasound images of muscle in terms of its measured state. Ultrasound data synchronized with electromyography of the calf muscles, with measures of joint torque/angle were recorded from 19 healthy participants (6 female, ages: 30 +- 7.7). A segmentation algorithm previously developed by our group was applied to extract a region of interest of the medial gastrocnemius. Then a deep convolutional neural network was trained to predict the measured states (joint angle/torque, electromyography) directly from the segmented images. Results revealed for the first time that active and passive muscle states can be measured directly from standard b-mode ultrasound images, accurately predicting for a held out test participant changes in the joint angle, electromyography, and torque with as little error as 0.022, 0.0001V, 0.256Nm (root mean square error) respectively.
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