{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T22:42:10Z","timestamp":1784673730269,"version":"3.55.0"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T00:00:00Z","timestamp":1592438400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T00:00:00Z","timestamp":1592438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2020,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n<jats:title>Purpose<\/jats:title>\n<jats:p>In the field of medical image analysis, deep learning methods gained huge attention over the last years. This can be explained by their often improved performance compared to classic explicit algorithms. In order to work well, they need large amounts of annotated data for supervised learning, but these are often not available in the case of medical image data. One way to overcome this limitation is to generate synthetic training data, e.g., by performing simulations to artificially augment the dataset. However, simulations require domain knowledge and are limited by the complexity of the underlying physical model. Another method to perform data augmentation is the generation of images by means of neural networks.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Methods<\/jats:title>\n<jats:p>We developed a new algorithm for generation of synthetic medical images exhibiting speckle noise via generative adversarial networks (GANs). Key ingredient is a speckle layer, which can be incorporated into a neural network in order to add realistic and domain-dependent speckle. We call the resulting GAN architecture SpeckleGAN.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>We compared our new approach to an equivalent GAN without speckle layer. SpeckleGAN was able to generate ultrasound images with very crisp speckle patterns in contrast to the baseline GAN, even for small datasets of 50 images. SpeckleGAN outperformed the baseline GAN by up to 165\u00a0% with respect to the Fr\u00e9chet Inception distance. For artery layer and lumen segmentation, a performance improvement of up to 4\u00a0% was obtained for small datasets, when these were augmented with images by SpeckleGAN.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusion<\/jats:title>\n<jats:p>SpeckleGAN facilitates the generation of realistic synthetic ultrasound images to augment small training sets for deep learning based image processing. Its application is not restricted to ultrasound images but could be used for every imaging methodology that produces images with speckle such as optical coherence tomography or radar.<\/jats:p>\n<\/jats:sec>","DOI":"10.1007\/s11548-020-02203-1","type":"journal-article","created":{"date-parts":[[2020,6,18]],"date-time":"2020-06-18T14:04:00Z","timestamp":1592489040000},"page":"1427-1436","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0610-0347","authenticated-orcid":false,"given":"Lennart","family":"Bargsten","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Schlaefer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,6,18]]},"reference":[{"issue":"2","key":"2203_CR1","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.compmedimag.2013.07.001","volume":"38","author":"S Balocco","year":"2014","unstructured":"Balocco S, Gatta C, Ciompi F, Wahle A, Radeva P, Carlier S, Unal G, Sanidas E, Mauri J, Carillo X, Kovarnik T, Wang CW, Chen HC, Exarchos TP, Fotiadis DI, Destrempes F, Cloutier G, Pujol O, Alberti M, Mendizabal-Ruiz EG, Rivera M, Aksoy T, Downe RW, Kakadiaris IA (2014) Standardized evaluation methodology and reference database for evaluating ivus image segmentation. Comput Med Imaging Graph 38(2):70\u201390","journal-title":"Comput Med Imaging Graph"},{"issue":"1","key":"2203_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/T-SU.1978.30978","volume":"25","author":"C Burckhardt","year":"1978","unstructured":"Burckhardt C (1978) Speckle in ultrasound b-mode scans. IEEE Trans Son Ultrason 25(1):1\u20136","journal-title":"IEEE Trans Son Ultrason"},{"key":"2203_CR3","doi-asserted-by":"crossref","unstructured":"China D, Mitra P, Sheet D (2017) Segmentation of lumen and external elastic laminae in intravascular ultrasound images using ultrasonic backscattering physics initialized multiscale random walks. In: Computer vision, graphics, and image processing. Springer, New York, pp 393\u2013403","DOI":"10.1007\/978-3-319-68124-5_34"},{"key":"2203_CR4","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L, Li K Li F-F: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"2203_CR5","unstructured":"Dubuisson MP, Jain A (1994) A modified hausdorff distance for object matching. In: Proceedings of the 12th international conference on pattern recognition, pp 566\u2013568"},{"key":"2203_CR6","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Advances in neural information processing systems. Curran Associates Inc, vol 27, pp 2672\u20132680"},{"key":"2203_CR7","volume-title":"Introduction to fourier optics","author":"JW Goodman","year":"1968","unstructured":"Goodman JW (1968) Introduction to fourier optics. McGraw-Hill, New York"},{"key":"2203_CR8","volume-title":"Speckle phenomena in optics: theory and applications","author":"JW Goodman","year":"2007","unstructured":"Goodman JW (2007) Speckle phenomena in optics: theory and applications. Roberts and Company Publishers, Englewood"},{"key":"2203_CR9","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2203_CR10","unstructured":"Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in neural information processing systems. Curran Associates Inc, vol 30, pp 6626\u20136637"},{"issue":"10","key":"2203_CR11","doi-asserted-by":"publisher","first-page":"2318","DOI":"10.1109\/TKDE.2017.2720168","volume":"29","author":"A Karpatne","year":"2017","unstructured":"Karpatne A, Atluri G, Faghmous JH, Steinbach M, Banerjee A, Ganguly A, Shekhar S, Samatova N, Kumar V (2017) Theory-guided data science: a new paradigm for scientific discovery from data. IEEE Trans Knowl Data Eng 29(10):2318\u20132331","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2203_CR12","volume-title":"Global atlas on cardiovascular disease prevention and control","author":"S Mendis","year":"2011","unstructured":"Mendis S, Puska P, Norrving B, Organization WH, Federation WH, Organization WS (2011) Global atlas on cardiovascular disease prevention and control. World Health Organization, Geneva"},{"key":"2203_CR13","doi-asserted-by":"crossref","unstructured":"Middel L, Palm C, Erdt M (2019) Synthesis of medical images using gans. In: First international workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, Held in Conjunction with MICCAI 2019, pp 125\u2013134","DOI":"10.1007\/978-3-030-32689-0_13"},{"key":"2203_CR14","unstructured":"Miyato T, Kataoka T, Koyama M, Yoshida Y (2018) Spectral normalization for generative adversarial networks. In: 6th international conference on learning representations (ICLR 2018)"},{"key":"2203_CR15","doi-asserted-by":"crossref","unstructured":"Park T, Liu MY, Wang TC, Zhu JY (2019) Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2019.00244"},{"key":"2203_CR16","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention (MICCAI), Springer, Berlin, vol 9351, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2203_CR17","unstructured":"Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X, Chen X (2016) Improved techniques for training gans. In: Advances in neural information processing systems 29, pp 2234\u20132242. Curran Associates, Inc"},{"key":"2203_CR18","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1186\/s40537-019-0197-0","volume":"6","author":"C Shorten","year":"2019","unstructured":"Shorten C, Khoshgoftaar TM (2019) A survey on image data augmentation for deep learning. J Big Data 6:50","journal-title":"J Big Data"},{"key":"2203_CR19","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"2203_CR20","doi-asserted-by":"crossref","unstructured":"Tom F, Sheet D (2008) Simulating patho-realistic ultrasound images using deep generative networks with adversarial learning. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), pp 1174\u20131177 (2018)","DOI":"10.1109\/ISBI.2018.8363780"},{"key":"2203_CR21","doi-asserted-by":"crossref","unstructured":"Uzunova H, Ehrhardt J, Jacob F, Frydrychowicz A, Handels H (2019) Multi-scale gans for memory-efficient generation of high resolution medical images. In: Medical image computing and computer assisted intervention (MICCAI), pp 112\u2013120. Springer, New York (2019)","DOI":"10.1007\/978-3-030-32226-7_13"},{"key":"2203_CR22","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.ultras.2019.03.014","volume":"96","author":"J Yang","year":"2019","unstructured":"Yang J, Faraji M, Basu A (2019) Robust segmentation of arterial walls in intravascular ultrasound images using dual path u-net. Ultrasonics 96:24\u201333","journal-title":"Ultrasonics"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-020-02203-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-020-02203-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-020-02203-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,18]],"date-time":"2021-06-18T00:03:42Z","timestamp":1623974622000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-020-02203-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,18]]},"references-count":22,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2020,9]]}},"alternative-id":["2203"],"URL":"https:\/\/doi.org\/10.1007\/s11548-020-02203-1","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,18]]},"assertion":[{"value":"12 January 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}