-
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 ac...
read it
-
Estimation of preterm birth markers with U-Net segmentation network
Preterm birth is the most common cause of neonatal death. Current diagno...
read it
-
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...
read it
-
Synthetic Elastography using B-mode Ultrasound through a Deep Fully-Convolutional Neural Network
Shear-wave elastography (SWE) permits local estimation of tissue elastic...
read it
-
Automatic Recognition of the Supraspinatus Tendinopathy from Ultrasound Images using Convolutional Neural Networks
Tendon injuries like tendinopathies, full and partial thickness tears ar...
read it
-
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content
High dynamic range (HDR) imaging provides the capability of handling rea...
read it
-
Simultaneous Segmentation and Classification of Bone Surfaces from Ultrasound Using a Multi-feature Guided CNN
Various imaging artifacts, low signal-to-noise ratio, and bone surfaces ...
read it
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 skeletal muscle states using ultrasonic imaging. Background: Current technology (electromyography, dynamometry, shear wave imaging) provides no general, non-invasive method for online estimation of skeletal intramuscular states. Ultrasound (US) allows non-invasive imaging of muscle, yet current computational approaches have never achieved simultaneous extraction nor generalisation of independently varying, active and passive states. We use deep learning to investigate the generalizable content of 2D US muscle images. Method: US data synchronized with electromyography of the calf muscles, with measures of joint moment/angle were recorded from 32 healthy participants (7 female, ages: 27.5, 19-65). We extracted a region of interest of medial gastrocnemius and soleus using our prior developed accurate segmentation algorithm. From the segmented images, a deep convolutional neural network was trained to predict three absolute, drift-free, components of the neurobiomechanical state (activity, joint angle, joint moment) during experimentally designed, simultaneous, independent variation of passive (joint angle) and active (electromyography) inputs. Results: For all 32 held-out participants (16-fold cross-validation) the ankle joint angle, electromyography, and joint moment were estimated to accuracy 55+-8 and 46+-9 can encode in generalizable form, the activity-length-tension state relationship of muscle. Observation only, low power, 2D US imaging can provide a new category of technology for non-invasive estimation of neural output, length and tension in skeletal muscle. This proof of principle has value for personalised muscle diagnosis in pain, injury, neurological conditions, neuropathies, myopathies and ageing.
READ FULL TEXT
Comments
There are no comments yet.