{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T14:55:10Z","timestamp":1776956110049,"version":"3.51.4"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s11517-023-02846-7","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T04:01:46Z","timestamp":1688961706000},"page":"2677-2697","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Plane invariant segmentation of computed tomography images through weighted cross entropy optimized conditional GANs in compressed formats"],"prefix":"10.1007","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7312-0028","authenticated-orcid":false,"given":"Usman","family":"Khan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amanullah","family":"Yasin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,10]]},"reference":[{"key":"2846_CR1","doi-asserted-by":"crossref","unstructured":"Tan Y (2021) GPU-based parallel implementation of swarm intelligence algorithms, 1st ed. 2016. Accessed 14 May 2021","DOI":"10.1016\/B978-0-12-809362-7.50001-7"},{"key":"2846_CR2","doi-asserted-by":"publisher","unstructured":"Goodfellow et al (2021) Generative adversarial networks, Communications of the ACM, vol 63 no. 11, pp. 139-144, 2020. Available: https:\/\/doi.org\/10.1145\/3422622 Accessed 15 Mar 2021","DOI":"10.1145\/3422622"},{"key":"2846_CR3","doi-asserted-by":"publisher","unstructured":"Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models, International Journal of Computer Vision, vo. 1, no 4, pp 321-331 Available: https:\/\/doi.org\/10.1007\/bf00133570 Accessed 20 Jun 2021","DOI":"10.1007\/bf00133570"},{"key":"2846_CR4","doi-asserted-by":"publisher","unstructured":"Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Transactions on pattern analysis and machine intelligence, vol 24, no 5, pp 603-619, Available: https:\/\/doi.org\/10.1109\/34.1000236 Accessed 18 Mar 2021","DOI":"10.1109\/34.1000236"},{"key":"2846_CR5","doi-asserted-by":"publisher","unstructured":"Osher S, Sethian J (1988) Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics, vol 79, no 1, pp 12-49, Available: https:\/\/doi.org\/10.1016\/0021-9991(88)900022 Accessed 24 Mar 2021","DOI":"10.1016\/0021-9991(88)900022"},{"key":"2846_CR6","unstructured":"Lecun Y, Bengio Y (1995) Convolutional networks for images, speech, and time series. The handbook of brain theory and neural networks, pp 255-258 Accessed: 25 Mar 2021"},{"key":"2846_CR7","doi-asserted-by":"publisher","unstructured":"Stehling M, Turner R, Mansfield P (1991) Echo-planar imaging: magnetic resonance imaging in a fraction of a second. Science, vol 254, no 5028, pp 43-50 Available: https:\/\/doi.org\/10.1126\/science.1925560Accessed: 8 Apr 2021","DOI":"10.1126\/science.1925560"},{"key":"2846_CR8","doi-asserted-by":"publisher","unstructured":"McGibney G, Smith M, Nichols S, Crawley A (1993) Quantitative evaluation of several partial Fourier reconstruction algorithms used in MRI. Magnetic Resonance in Medicine, vol 30, no 1, pp 51-59 Available: https:\/\/doi.org\/10.1002\/mrm.1910300109 Accessed 10 Apr 2021","DOI":"10.1002\/mrm.1910300109"},{"key":"2846_CR9","doi-asserted-by":"publisher","unstructured":"Yang G et al (2018) DAGAN: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction. IEEE Transactions on Medical Imaging, vol. 37, no. 6, pp 1310-1321 Available: https:\/\/doi.org\/10.1109\/tmi.2017.2785879 Accessed 18 Apr 2021","DOI":"10.1109\/tmi.2017.2785879"},{"key":"2846_CR10","unstructured":"VerSe\u201819 - Grand Challenge, grand-challenge.org, 2021. [Online]. Available: https:\/\/verse2019.grand-challenge.org Accessed: 20 Apr 2021"},{"key":"2846_CR11","unstructured":"https:\/\/www.socr.umich.edu Brain Viewer Webapp, Socr.umich.edu, 2021. [Online]. Available: https:\/\/socr.umich.edu\/HTML5\/BrainViewer Accessed: 20 Apr 2021"},{"key":"2846_CR12","unstructured":"Wang X et al (2018) ESRGAN: enhanced super-resolution generative adversarial networks European conference on computer vision vol 2018 Accessed 23 Apr 2021"},{"key":"2846_CR13","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2017.19","author":"C Ledig","year":"2017","unstructured":"Ledig C, Theis L, Huszar F, Caballero J, Cunningham A, Acosta A, Shi W (2017) Photo-realistic single image super-resolution using a generative adversarial network. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https:\/\/doi.org\/10.1109\/cvpr.2017.19","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"2846_CR14","doi-asserted-by":"publisher","unstructured":"Zhao Y et al (2020) Fine-grained facial image-to-image translation with an attention-based pipeline generative adversarial framework. Multimedia Tools and Applications, vol. 79, no. 21-22 pp. 14981-15000 Available: https:\/\/doi.org\/10.1007\/s11042-019-08346-x Accessed 23 Apr 2021","DOI":"10.1007\/s11042-019-08346-x"},{"key":"2846_CR15","doi-asserted-by":"publisher","unstructured":"Singh N, Raza K (2021) Medical image generation using generative adversarial networks: a review health informatics: a computational perspective in healthcare, pp. 77-96 Available: https:\/\/doi.org\/10.1007\/978-981-15-9735-0-5 Accessed 24 Apr 2021","DOI":"10.1007\/978-981-15-9735-0-5"},{"key":"2846_CR16","unstructured":"Radford A, Metz L (2016) Unsupervised representation learning with deep convolutional generative adversarial networks. International Conference on Learning Representations, vol 2 Accessed 24 Apr 2021"},{"key":"2846_CR17","unstructured":"Denton E, Chintala S, Szlam A, Fergus R (2015) Deep generative image models using a laplacian pyramid of adversarial networks. Advances in Neural Information Processing Systems 28 (NIPS 2015)"},{"key":"2846_CR18","doi-asserted-by":"publisher","unstructured":"Sheng M, Ma Z, Jia H, Mao Q, Dong M (2020) Face aging with conditional generative adversarial network guided by ranking-CNN. 2020 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). https:\/\/doi.org\/10.1109\/mipr49039.2020.00071","DOI":"10.1109\/mipr49039.2020.00071"},{"key":"2846_CR19","doi-asserted-by":"publisher","unstructured":"Zhu J, Park T, Isola P, Efros A (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks 2017 IEEE International Conference on Computer Vision (ICCV) Available: https:\/\/doi.org\/10.1109\/iccv.2017.244 Accessed 25 Apr 2021","DOI":"10.1109\/iccv.2017.244"},{"key":"2846_CR20","doi-asserted-by":"publisher","unstructured":"Kang T, Lee KH (2020) Unsupervised image-to-image translation with self-attention networks. IEEE International Conference on Big Data and Smart Computing (BigComp). https:\/\/doi.org\/10.1109\/bigcomp48618.2020.00-92","DOI":"10.1109\/bigcomp48618.2020.00-92"},{"key":"2846_CR21","doi-asserted-by":"publisher","unstructured":"Brehm S, Scherer S, Lienhart R (2022) Semantically consistent image-to-image translation for unsupervised domain adaptation. Proceedings of the 14th International Conference on Agents and Artificial Intelligence. https:\/\/doi.org\/10.5220\/0010786000003116","DOI":"10.5220\/0010786000003116"},{"issue":"8","key":"2846_CR22","doi-asserted-by":"publisher","first-page":"2092","DOI":"10.1109\/tmi.2022.3156614","volume":"41","author":"Y Zhou","year":"2022","unstructured":"Zhou Y, Yang Z, Zhang H, Chang EI, Fan Y, Xu Y (2022) 3D segmentation guided style-based generative adversarial networks for pet synthesis. IEEE Transactions on Medical Imaging 41(8):2092\u20132104. https:\/\/doi.org\/10.1109\/tmi.2022.3156614","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2846_CR23","doi-asserted-by":"publisher","unstructured":"Liao Y, Huang Y (2022) Deep learning-based application of image style transfer. Mathematical Problems in Engineering 1\u201310. https:\/\/doi.org\/10.1155\/2022\/1693892","DOI":"10.1155\/2022\/1693892"},{"key":"2846_CR24","unstructured":"Wang Y (2020) A mathematical introduction to generative adversarial nets (GAN) arXiv e-prints Accessed 29 Apr 2021"},{"key":"2846_CR25","unstructured":"Grover P (2021) 5 regression loss functions all machine learners should know medium [online] Available: https:\/\/heartbeat.fritz.ai\/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9d4b0 Accessed 28 Apr 2021"},{"key":"2846_CR26","doi-asserted-by":"publisher","unstructured":"L\u00f6ffler M et al (2020) A vertebral segmentation dataset with fracture grading, Radiology: Artificial Intelligence, vol 2 no 4, p e190138 Available: https:\/\/doi.org\/10.1148\/ryai.2020190138 Accessed 30 Apr 2021","DOI":"10.1148\/ryai.2020190138"},{"key":"2846_CR27","unstructured":"Sekuboyina A (2020) VerSe: a vertebrae labelling and segmentation benchmark for multi-detector CT images, arXiv e-prints. Accessed 10 Mar 2021"},{"key":"2846_CR28","doi-asserted-by":"publisher","unstructured":"Gorgolewski K et al The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments\", Scientific Data, vol 3 no 1, 2016. Available: https:\/\/doi.org\/10.1038\/sdata.2016.44 Accessed 20 Jul 2021","DOI":"10.1038\/sdata.2016.44"},{"issue":"02","key":"2846_CR29","doi-asserted-by":"publisher","first-page":"2150006","DOI":"10.1142\/s0219720021500062","volume":"19","author":"F Nazem","year":"2021","unstructured":"Nazem F, Ghasemi F, Fassihi A, Dehnavi A (2021) 3D U-Net: A voxel-based method in binding site prediction of protein structure. Journal Of Bioinformatics And Computational Biology 19(02):2150006. https:\/\/doi.org\/10.1142\/s0219720021500062","journal-title":"Journal Of Bioinformatics And Computational Biology"},{"key":"2846_CR30","doi-asserted-by":"publisher","unstructured":"Dutta A, Singh K, Anand A (2021) SpliceViNCI: Visualizing the splicing of non-canonical introns through recurrent neural networks. Journal Of Bioinformatics And Computational Biology, 19(04) 2150014. https:\/\/doi.org\/10.1142\/s0219720021500141","DOI":"10.1142\/s0219720021500141"},{"key":"2846_CR31","unstructured":"Maas A, Hannun A, Ng A (2013) Rectifier nonlinearities improve neural network, acoustic models. ICML, 30"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02846-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02846-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02846-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T05:14:08Z","timestamp":1695791648000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02846-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,10]]},"references-count":31,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["2846"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02846-7","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,10]]},"assertion":[{"value":"25 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}