{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T10:05:38Z","timestamp":1767866738364,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,1,29]],"date-time":"2018-01-29T00:00:00Z","timestamp":1517184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>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\u201336, 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\u00b0 error, which was an improvement on previous methods.<\/jats:p>","DOI":"10.3390\/jimaging4020029","type":"journal-article","created":{"date-parts":[[2018,1,29]],"date-time":"2018-01-29T07:46:20Z","timestamp":1517211980000},"page":"29","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Estimating Full Regional Skeletal Muscle Fibre Orientation from B-Mode Ultrasound Images Using Convolutional, Residual, and Deconvolutional Neural Networks"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6883-6515","authenticated-orcid":false,"given":"Ryan","family":"Cunningham","sequence":"first","affiliation":[{"name":"School of Healthcare Science, Manchester Metropolitan University, Manchester M15 6BH, UK"}]},{"given":"Mar\u00eda","family":"S\u00e1nchez","sequence":"additional","affiliation":[{"name":"School of Healthcare Science, Manchester Metropolitan University, Manchester M15 6BH, UK"}]},{"given":"Gregory","family":"May","sequence":"additional","affiliation":[{"name":"School of Healthcare Science, Manchester Metropolitan University, Manchester M15 6BH, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8125-6320","authenticated-orcid":false,"given":"Ian","family":"Loram","sequence":"additional","affiliation":[{"name":"School of Healthcare Science, Manchester Metropolitan University, Manchester M15 6BH, UK"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/TBME.2015.2465168","article-title":"Ultrasound-based detection of fasciculations in healthy and diseased muscles","volume":"63","author":"Harding","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_2","unstructured":"Harding, P.J., Hodson-Tole, E.F., Cunningham, R., Loram, I., and Costen, N. (2012, January 11\u201315). Automated detection of skeletal muscle twitches from B-mode ultrasound images: An application to motor neuron disease. Proceedings of the 21st International Conference on Pattern Recognition (ICPR), Tsukuba, Japan."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TNSRE.2016.2641024","article-title":"Proactive selective inhibition targeted at the neck muscles: This proximal constraint facilitates learning and regulates global control","volume":"25","author":"Loram","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1152\/japplphysiol.01229.2005","article-title":"Use of ultrasound to make noninvasive in vivo measurement of continuous changes in human muscle contractile length","volume":"100","author":"Loram","year":"2006","journal-title":"J. Appl. Physiol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.ultras.2014.10.020","article-title":"Automatic measurement of pennation angle and fascicle length of gastrocnemius muscles using real-time ultrasound imaging","volume":"57","author":"Zhou","year":"2015","journal-title":"Ultrasonics"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2068","DOI":"10.1016\/j.jbiomech.2009.06.003","article-title":"Automated tracking of muscle fascicle orientation in B-mode ultrasound images","volume":"42","author":"Rana","year":"2009","journal-title":"J. Biomech."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1016\/j.jbiomech.2011.07.017","article-title":"Computational methods for quantifying in vivo muscle fascicle curvature from ultrasound images","volume":"44","author":"Namburete","year":"2011","journal-title":"J. Biomech."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.bspc.2015.04.016","article-title":"Continuous fascicle orientation measurement of medial gastrocnemius muscle in ultrasonography using frequency domain Radon transform","volume":"20","author":"Chen","year":"2015","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1109\/TBME.2013.2245328","article-title":"Estimating skeletal muscle fascicle curvature from B-mode ultrasound image sequences","volume":"60","author":"Darby","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1113\/jphysiol.1995.sp020683","article-title":"Changes in pennation with joint angle and muscle torque: In vivo measurements in human brachialis muscle","volume":"484","author":"Herbert","year":"1995","journal-title":"J. Physiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1113\/jphysiol.1996.sp021685","article-title":"In vivo human gastrocnemius architecture with changing joint angle at rest and during graded isometric contraction","volume":"496","author":"Narici","year":"1996","journal-title":"J. Physiol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1002\/1097-4598(200011)23:11<1647::AID-MUS1>3.0.CO;2-M","article-title":"Functional and clinical significance of skeletal muscle architecture","volume":"23","author":"Lieber","year":"2000","journal-title":"Muscle Nerve"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/1475-925X-11-63","article-title":"Dynamic measurement of pennation angle of gastrocnemius muscles during contractions based on ultrasound imaging","volume":"11","author":"Zhou","year":"2012","journal-title":"Biomed. Eng. Online"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2828","DOI":"10.1109\/TBME.2015.2445345","article-title":"Automatic fascicle length estimation on muscle ultrasound images with an orientation-sensitive segmentation","volume":"62","author":"Zhou","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/S0262-8856(03)00070-2","article-title":"Hand gesture recognition using a real-time tracking method and hidden Markov models","volume":"21","author":"Chen","year":"2003","journal-title":"Image Vis. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1474","DOI":"10.1016\/j.ultrasmedbio.2008.02.009","article-title":"Estimation of muscle fiber orientation in ultrasound images using Revoting Hough Transform (RVHT)","volume":"34","author":"Zhou","year":"2008","journal-title":"Ultrasound Med. Biol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1016\/j.jbiomech.2010.07.031","article-title":"A novel method of studying fascicle architecture in relaxed and contracted muscles","volume":"43","author":"Stark","year":"2010","journal-title":"J. Biomech."},{"key":"ref_18","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_20","first-page":"1929","article-title":"Dropout\u202f: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Hinton","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_21","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified Linear Units Improve Restricted Boltzmann Machines. Proceedings of the 27th International Conference on International Conference on Machine Learning, Haifa, Israel."},{"key":"ref_22","unstructured":"Jackel, L.D.L., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Le Cun, B., Denker, J., and Henderson, D. (1990). Handwritten Digit Recognition with a Back-Propagation Network. Advances in Neural Information Processing Systems 2, Morgan Kaufmann Publishers Inc."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cunningham, R.J., Harding, P.J., and Loram, I.D. (2017). Deep residual networks for quantification of muscle fiber orientation and curvature from ultrasound. Medical Image Understanding and Analysis, Springer.","DOI":"10.1007\/978-3-319-60964-5_6"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1109\/TMI.2016.2623819","article-title":"Real-Time Ultrasound Segmentation, Analysis and Visualization of Deep Cervical Muscle Structure","volume":"36","author":"Cunningham","year":"2017","journal-title":"Trans. Med. Imaging"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Pfister, T., Charles, J., and Zisserman, A. (2015, January 7\u201313). Flowing convnets for human pose estimation in videos. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.222"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fischer, P., Dosovitskiy, A., Ilg, E., H\u00e4usser, P., Haz\u0131rba\u015f, C., Golkov, V., van der Smagt, P., Cremers, D., and Brox, T. (2015, January 7\u201313). FlowNet: Learning Optical Flow with Convolutional Networks. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.316"},{"key":"ref_27","first-page":"230","article-title":"Regressing heatmaps for multiple landmark localization using CNNs","volume":"Volume 9901 LNCS","author":"Payer","year":"2016","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 7\u201313). Learning deconvolution network for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision, Washington, DC, USA.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_29","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Ronneberger","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention (MICCAI)"},{"key":"ref_30","first-page":"818","article-title":"Visualizing and Understanding Convolutional Networks. arXiv:1311.2901v3 [cs.CV] 28 November 2013","volume":"8689","author":"Zeiler","year":"2014","journal-title":"Comput. Vis. Pattern Recognit."},{"key":"ref_31","first-page":"130","article-title":"Multiscale vessel enhancement filtering","volume":"Volume 1496","author":"Frangi","year":"1998","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI\u201998. MICCAI 1998. Lecture Notes in Computer Science"},{"key":"ref_32","unstructured":"Kroon, D.J. (2017, November 01). Hessian based Frangi Vesselness filter. Available online: https:\/\/uk.mathworks.com\/matlabcentral\/fileexchange\/24409-hessian-based-frangi-vesselness-filter."},{"key":"ref_33","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia, Italy."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1152\/japplphysiol.00701.2011","article-title":"Automated regional analysis of B-mode ultrasound images of skeletal muscle movement","volume":"112","author":"Darby","year":"2012","journal-title":"J. Appl. Physiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1249\/JES.0000000000000049","article-title":"Elastography for Muscle Biomechanics: Toward the Estimation of Individual Muscle Force","volume":"43","author":"Hug","year":"2015","journal-title":"Exerc. Sport Sci. Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1016\/j.jbiomech.2013.07.033","article-title":"Validation of shear wave elastography in skeletal muscle","volume":"46","author":"Eby","year":"2013","journal-title":"J. Biomech."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/4\/2\/29\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:52:54Z","timestamp":1760194374000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/4\/2\/29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,29]]},"references-count":36,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2018,2]]}},"alternative-id":["jimaging4020029"],"URL":"https:\/\/doi.org\/10.3390\/jimaging4020029","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,29]]}}}