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dc.contributor.authorCronin, NJ
dc.contributor.authorFinni, T
dc.contributor.authorSeynnes, O
dc.date.accessioned2020-06-25T21:44:46Z
dc.date.available2020-06-25T21:44:46Z
dc.date.issued2020
dc.identifier.issn0169-2607
dc.identifier.doi10.1016/j.cmpb.2020.105583
dc.identifier.urihttp://hdl.handle.net/10072/394899
dc.description.abstractBackground and objective: Deep learning approaches are common in image processing, but often rely on supervised learning, which requires a large volume of training images, usually accompanied by hand-crafted labels. As labelled data are often not available, it would be desirable to develop methods that allow such data to be compiled automatically. In this study, we used a Generative Adversarial Network (GAN) to generate realistic B-mode musculoskeletal ultrasound images, and tested the suitability of two automated labelling approaches. Methods: We used a model including two GANs each trained to transfer an image from one domain to another. The two inputs were a set of 100 longitudinal images of the gastrocnemius medialis muscle, and a set of 100 synthetic segmented masks that featured two aponeuroses and a random number of ‘fascicles’. The model output a set of synthetic ultrasound images and an automated segmentation of each real input image. This automated segmentation process was one of the two approaches we assessed. The second approach involved synthesising ultrasound images and then feeding these images into an ImageJ/Fiji-based automated algorithm, to determine whether it could detect the aponeuroses and muscle fascicles. Results: Histogram distributions were similar between real and synthetic images, but synthetic images displayed less variation between samples and a narrower range. Mean entropy values were statistically similar (real: 6.97, synthetic: 7.03; p = 0.218), but the range was much narrower for synthetic images (6.91 – 7.11 versus 6.30 – 7.62). When comparing GAN-derived and manually labelled segmentations, intersection-over-union values- denoting the degree of overlap between aponeurosis labels- varied between 0.0280 – 0.612 (mean ± SD: 0.312 ± 0.159), and pennation angles were higher for the GAN-derived segmentations (25.1° vs. 19.3°; p < 0.001). For the second segmentation approach, the algorithm generally performed equally well on synthetic and real images, yielding pennation angles within the physiological range (13.8–20°). Conclusions: We used a GAN to generate realistic B-mode ultrasound images, and extracted muscle architectural parameters from these images automatically. This approach could enable generation of large labelled datasets for image segmentation tasks, and may also be useful for data sharing. Automatic generation and labelling of ultrasound images minimises user input and overcomes several limitations associated with manual analysis.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofjournalComputer Methods and Programs in Biomedicine
dc.relation.ispartofvolume196
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchBiomedical engineering
dc.subject.fieldofresearchcode4602
dc.subject.fieldofresearchcode4003
dc.titleUsing deep learning to generate synthetic B-mode musculoskeletal ultrasound images
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationCronin, NJ; Finni, T; Seynnes, O, Using deep learning to generate synthetic B-mode musculoskeletal ultrasound images, Computer Methods and Programs in Biomedicine, 2020, 196
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2020-06-25T02:58:46Z
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2020 The Author(s). Published by Elsevier B.V. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorCronin, Neil


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