Next Article in Journal
Illusion and Illusoriness of Color and Coloration
Next Article in Special Issue
Image Features Based on Characteristic Curves and Local Binary Patterns for Automated HER2 Scoring
Previous Article in Journal
Exploiting Multiple Detections for Person Re-Identification
Previous Article in Special Issue
Stable Image Registration for In-Vivo Fetoscopic Panorama Reconstruction
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
J. Imaging 2018, 4(2), 29; https://doi.org/10.3390/jimaging4020029

Estimating Full Regional Skeletal Muscle Fibre Orientation from B-Mode Ultrasound Images Using Convolutional, Residual, and Deconvolutional Neural Networks

School of Healthcare Science, Manchester Metropolitan University, Manchester M15 6BH, UK
This article is an extended version of our paper published in Cunningham, R.J.; Harding, P.J.; Loram, I.D. Deep residual networks for quantification of muscle fiber orientation and curvature from ultrasound. In Medical Image Understanding and Analysis; Springer: Cham, Switzerland, 2017; pp. 63–73.
*
Authors to whom correspondence should be addressed.
Received: 8 November 2017 / Revised: 17 January 2018 / Accepted: 22 January 2018 / Published: 29 January 2018
(This article belongs to the Special Issue Selected Papers from “MIUA 2017”)
View Full-Text   |   Download PDF [6231 KB, uploaded 30 January 2018]   |  

Abstract

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. View Full-Text
Keywords: ultrasound; B-mode; skeletal muscle; fascicle orientation; pennation angle; fibre orientation; fibre tract; fascicle tract; convolutional neural network; deconvolutional neural network; residual neural network; deep learning ultrasound; B-mode; skeletal muscle; fascicle orientation; pennation angle; fibre orientation; fibre tract; fascicle tract; convolutional neural network; deconvolutional neural network; residual neural network; deep learning
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Cunningham, R.; Sánchez, M.B.; May, G.; Loram, I. Estimating Full Regional Skeletal Muscle Fibre Orientation from B-Mode Ultrasound Images Using Convolutional, Residual, and Deconvolutional Neural Networks. J. Imaging 2018, 4, 29.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
J. Imaging EISSN 2313-433X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top