The application of deep convolutional neural networks to ultrasound for modelling of dynamic states within human skeletal muscle

by   Ryan J. Cunningham, et al.

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.


page 4

page 5

page 7

page 9


Prediction of Metacarpophalangeal joint angles and Classification of Hand configurations based on Ultrasound Imaging of the Forearm

With the advancement in computing and robotics, it is necessary to devel...

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...

Automatic segmentation of vertebral features on ultrasound spine images using Stacked Hourglass Network

Objective: The spinous process angle (SPA) is one of the essential param...

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...

Task-Invariant Learning of Continuous Joint Kinematics during Steady-State and Transient Ambulation Using Ultrasound Sensing

Natural control of limb motion is continuous and progressively adaptive ...

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 ...