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The Application of Deep Convolutional Neural Networks to Ultrasound for Modelling of Dynamic States within Human Skeletal Muscle

https://doi.org/10.1101/157479
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/mus.10375openalex-snapshot
Measurement of muscle contraction with ultrasound imaging
inferredcites →https://doi.org/10.1006/cviu.1995.1004openalex-snapshot
Active Shape Models-Their Training and Application
inferredcites →https://doi.org/10.1007/978-3-642-21735-7_7openalex-snapshot
Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction
inferredcites →https://doi.org/10.1007/bf00243510openalex-snapshot
Measurement of fibre pennation using ultrasound in the human quadriceps in vivo
inferredcites →https://doi.org/10.1007/bf02386190openalex-snapshot
Muscle cross-sectional area and voluntary force production characteristics in elite strength- and endurance-trained athletes and sprinters
inferredcites →https://doi.org/10.1007/s004210050510openalex-snapshot
Predictability of in vivo changes in pennation angle of human tibialis anterior muscle from rest to maximum isometric dorsiflexion
inferredcites →https://doi.org/10.1016/0885-2308(87)90026-xopenalex-snapshot
Learning sets of filters using back-propagation
inferredcites →https://doi.org/10.1016/j.jbiomech.2009.06.003openalex-snapshot
Automated tracking of muscle fascicle orientation in B-mode ultrasound images
inferredcites →https://doi.org/10.1016/j.jbiomech.2011.07.017openalex-snapshot
Computational methods for quantifying in vivo muscle fascicle curvature from ultrasound images
inferredcites →https://doi.org/10.1016/j.jbiomech.2013.07.033openalex-snapshot
Validation of shear wave elastography in skeletal muscle
inferredcites →https://doi.org/10.1016/s0301-5629(97)00281-0openalex-snapshot
Multilevel and Motion Model-Based Ultrasonic Speckle Tracking Algorithms
inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot
Deep learning
inferredcites →https://doi.org/10.1093/ptj/73.10.698openalex-snapshot
Electromyography: Some Methodological Problems and Issues
inferredcites →https://doi.org/10.1109/42.746627openalex-snapshot
Feature-adaptive motion tracking of ultrasound image sequences using a deformable mesh
inferredcites →https://doi.org/10.1109/cvpr.1994.323794openalex-snapshot
Good features to track
inferredcites →https://doi.org/10.1109/iccv.2015.123openalex-snapshot
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
inferredcites →https://doi.org/10.1109/iccv.2015.178openalex-snapshot
Learning Deconvolution Network for Semantic Segmentation
inferredcites →https://doi.org/10.1109/iccv.2015.316openalex-snapshot
FlowNet: Learning Optical Flow with Convolutional Networks
inferredcites →https://doi.org/10.1109/msp.2012.2205597openalex-snapshot
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
inferredcites →https://doi.org/10.1109/tbme.2013.2245328openalex-snapshot
Estimating Skeletal Muscle Fascicle Curvature From B-Mode Ultrasound Image Sequences
inferredcites →https://doi.org/10.1109/tmi.2006.877092openalex-snapshot
Ultrasound image segmentation: a survey
inferredcites →https://doi.org/10.1109/tnsre.2016.2641024openalex-snapshot
Proactive Selective Inhibition Targeted at the Neck Muscles: This Proximal Constraint Facilitates Learning and Regulates Global Control
inferredcites →https://doi.org/10.1126/science.1127647openalex-snapshot
Reducing the Dimensionality of Data with Neural Networks
inferredcites →https://doi.org/10.1126/science.7761831openalex-snapshot
The "Wake-Sleep" Algorithm for Unsupervised Neural Networks
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1152/japplphysiol.00701.2011openalex-snapshot
Automated regional analysis of B-mode ultrasound images of skeletal muscle movement
inferredcites →https://doi.org/10.1152/japplphysiol.01229.2005openalex-snapshot
Use of ultrasound to make noninvasive in vivo measurement of continuous changes in human muscle contractile length
inferredcites →https://doi.org/10.1162/089976602760128018openalex-snapshot
Training Products of Experts by Minimizing Contrastive Divergence
inferredcites →https://doi.org/10.1249/jes.0000000000000049openalex-snapshot
Elastography for Muscle Biomechanics
inferredcites →https://doi.org/10.1519/jsc.0b013e31815f2fd3openalex-snapshot
Muscle Force per Cross-sectional Area is Inversely Related with Pennation Angle in Strength Trained Athletes
inferredcites →https://doi.org/10.48550/arxiv.1311.2901openalex-snapshot
Visualizing and Understanding Convolutional Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-981-13-7564-4_16openalex-snapshot
Computer-Aided Speckle Noise Analysis in Ultrasound Images Through Fusion of Convolutional Neural Network and Wavelet Transform with Linear Discriminate Analysis
inferred← citeshttps://doi.org/10.1016/j.bspc.2021.103100openalex-snapshot
Personalized fusion of ultrasound and electromyography-derived neuromuscular features increases prediction accuracy of ankle moment during plantarflexion
inferred← citeshttps://doi.org/10.1017/wtc.2022.18openalex-snapshot
A deep learning method to predict ankle joint moment during walking at different speeds with ultrasound imaging: A framework for assistive devices control
inferred← citeshttps://doi.org/10.1038/s41598-020-60360-4openalex-snapshot
Ultrasound imaging links soleus muscle neuromechanics and energetics during human walking with elastic ankle exoskeletons
inferred← citeshttps://doi.org/10.1098/rsif.2019.0715openalex-snapshot
Estimation of absolute states of human skeletal muscle via standard B-mode ultrasound imaging and deep convolutional neural networks
inferred← citeshttps://doi.org/10.1109/jbhi.2020.2964098openalex-snapshot
Objective Analysis of Neck Muscle Boundaries for Cervical Dystonia Using Ultrasound Imaging and Deep Learning
inferred← citeshttps://doi.org/10.1109/tnsre.2021.3106900openalex-snapshot
A Dual-Modal Approach Using Electromyography and Sonomyography Improves Prediction of Dynamic Ankle Movement: A Case Study
inferred← citeshttps://doi.org/10.1109/tuffc.2024.3445434openalex-snapshot
Synthesizing Real-Time Ultrasound Images of Muscle Based on Biomechanical Simulation and Conditional Diffusion Network
inferred← citeshttps://doi.org/10.1113/jp276962openalex-snapshot
Neurocardiac regulation: from cardiac mechanisms to novel therapeutic approaches
inferred← citeshttps://doi.org/10.1371/journal.pone.0246611openalex-snapshot
Machine learning to extract muscle fascicle length changes from dynamic ultrasound images in real-time
inferred← citeshttps://doi.org/10.31224/osf.io/fsa3copenalex-snapshot
Ultrasound Segmentation of Cervical Muscle during head motion: a Dataset and a Benchmark using Deconvolutional Neural Networks
inferred← citeshttps://doi.org/10.31224/osf.io/pkdwtopenalex-snapshot
Automated analysis of neck muscle boundaries for cervical dystonia using ultrasound imaging and deep learning
inferred← citeshttps://doi.org/10.3390/app11041534openalex-snapshot
The Feasibility of Dynamic Musculoskeletal Function Analysis of the Vastus Lateralis in Endurance Runners Using Continuous, Hands-Free Ultrasound
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