{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:35:36Z","timestamp":1762432536799,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031777301"},{"type":"electronic","value":"9783031777318"}],"license":[{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,14]],"date-time":"2024-11-14T00:00:00Z","timestamp":1731542400000},"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":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-77731-8_29","type":"book-chapter","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T16:42:19Z","timestamp":1732034539000},"page":"313-324","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Using Diffusion Models for\u00a0Data Augmentation on\u00a0Limited Rodent OCT Datasets"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5337-8774","authenticated-orcid":false,"given":"Fernando","family":"Garc\u00eda-Torres","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roc\u00edo","family":"del Amor","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sandra","family":"Morales-Mart\u00ednez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c1lvaro","family":"Barroso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bj\u00f6rn","family":"Kemper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00fcrgen","family":"Schnekenburger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0181-3412","authenticated-orcid":false,"given":"Valery","family":"Naranjo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,14]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","unstructured":"T\u0103lu, S-D.:. Optical coherence tomography in the diagnosis and monitoring of retinal diseases. Int. Sch. Res. Not. 2013(1) (2013). https:\/\/doi.org\/10.1155\/2013\/910641","DOI":"10.1155\/2013\/910641"},{"key":"29_CR2","series-title":"Advances in Experimental Medicine and Biology","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/978-981-15-7627-0_10","volume-title":"Optical Imaging in Human Disease and Biological Research","author":"J Qin","year":"2021","unstructured":"Qin, J., An, L.: Optical coherence tomography for ophthalmology imaging. In: Wei, X., Gu, B. (eds.) Optical Imaging in Human Disease and Biological Research. AEMB, vol. 3233, pp. 197\u2013216. Springer, Singapore (2021). https:\/\/doi.org\/10.1007\/978-981-15-7627-0_10"},{"key":"29_CR3","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-981-15-7644-7_4","volume-title":"Macular Surgery","author":"CS Tan","year":"2020","unstructured":"Tan, C.S., Lim, L.W., Sadda, S.V.R.: Optical coherence tomography angiography in macular disorders. In: Chang, A., Mieler, W.F., Ohji, M. (eds.) Macular Surgery, pp. 45\u201364. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-7644-7_4"},{"key":"29_CR4","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-3-030-16638-0_4","volume-title":"High Resolution Imaging in Microscopy and Ophthalmology","author":"PL M\u00fcller","year":"2019","unstructured":"M\u00fcller, P.L., Wolf, S., Dolz-Marco, R., Tafreshi, A., Schmitz-Valckenberg, S., Holz, F.G.: Ophthalmic diagnostic imaging: retina. In: Bille, J.F. (ed.) High Resolution Imaging in Microscopy and Ophthalmology, pp. 87\u2013106. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-16638-0_4"},{"key":"29_CR5","doi-asserted-by":"publisher","unstructured":"Leandro, I., et al.: OCT-based deep-learning models for the identification of retinal key signs. Sci. Rep. 13(1) (2023). https:\/\/doi.org\/10.1038\/s41598-023-41362-4","DOI":"10.1038\/s41598-023-41362-4"},{"key":"29_CR6","unstructured":"Ho, P.-G.: Image Segmentation. BoD - Books on Demand (2011). Google-Books-ID: vDiaDwAAQBAJ"},{"key":"29_CR7","doi-asserted-by":"publisher","unstructured":"Akil, M., Elloumi, Y., Kachouri, R.: Chapter 2 - Detection of retinal abnormalities in fundus image using CNN deep learning networks. In: El-Baz, A.S., Suri, J.S. (eds.) State of the Art in Neural Networks and their Applications. Academic Press (2021). https:\/\/doi.org\/10.1016\/B978-0-12-819740-0.00002-4","DOI":"10.1016\/B978-0-12-819740-0.00002-4"},{"key":"29_CR8","doi-asserted-by":"publisher","unstructured":"Morales, S., et al.: Retinal layer segmentation in rodent OCT images: local intensity profiles & fully convolutional neural networks. Comput. Methods Program. Biomed. 198 (2021). https:\/\/doi.org\/10.1016\/j.cmpb.2020.105788","DOI":"10.1016\/j.cmpb.2020.105788"},{"issue":"1","key":"29_CR9","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/s12938-019-0649-y","volume":"18","author":"A Diaz-Pinto","year":"2019","unstructured":"Diaz-Pinto, A., Morales, S., Naranjo, V., K\u00f6hler, T., Mossi, J.M., Navea, A.: CNNs for automatic glaucoma assessment using fundus images: an extensive validation. Biomed. Eng. Online 18(1), 29 (2019). https:\/\/doi.org\/10.1186\/s12938-019-0649-y","journal-title":"Biomed. Eng. Online"},{"key":"29_CR10","doi-asserted-by":"publisher","unstructured":"Del Amor, R., et al.: Towards automatic glaucoma assessment: an encoder-decoder CNN for retinal layer segmentation in rodent OCT images. In: 2019 27th European Signal Processing Conference (EUSIPCO), A Coruna, Spain, pp. 1\u20135. IEEE (2019). https:\/\/doi.org\/10.23919\/EUSIPCO.2019.8902794","DOI":"10.23919\/EUSIPCO.2019.8902794"},{"key":"29_CR11","doi-asserted-by":"publisher","unstructured":"Wang, H., Liu, W., Hu, Z., Li, X., Li, F., Duan, L.: Model eye tool for retinal optical coherence tomography instrument calibration. J. Innov. Opt. Health Sci. (2021). https:\/\/doi.org\/10.1142\/S1793545821500103","DOI":"10.1142\/S1793545821500103"},{"key":"29_CR12","doi-asserted-by":"publisher","unstructured":"Strupler, M., et al.: Toward an automated method for optical coherence tomography characterization. J. Biomed. Opt. 20(12), 126007 (2015). https:\/\/doi.org\/10.1117\/1.JBO.20.12.126007","DOI":"10.1117\/1.JBO.20.12.126007"},{"issue":"9","key":"29_CR13","doi-asserted-by":"publisher","first-page":"4421","DOI":"10.1364\/BOE.494271","volume":"14","author":"A Barroso","year":"2023","unstructured":"Barroso, A., et al.: Durable 3D murine ex vivo retina glaucoma models for optical coherence tomography. Biomed. Opt. Express 14(9), 4421\u20134438 (2023). https:\/\/doi.org\/10.1364\/BOE.494271","journal-title":"Biomed. Opt. Express"},{"key":"29_CR14","doi-asserted-by":"publisher","unstructured":"Barroso, A., et al.: Durable ex vivo mouse retina 3D tissue models for optical coherence tomography. In: Label-free Biomedical Imaging and Sensing (LBIS) 2024, vol. 12854, pp. 21\u201323. SPIE (2024). https:\/\/doi.org\/10.1117\/12.3002538","DOI":"10.1117\/12.3002538"},{"key":"29_CR15","doi-asserted-by":"publisher","unstructured":"Zha, X., Shi, F., Ma, Y., Zhu, W., Chen, X.: Generation of retinal OCT images with diseases based on cGAN. In: Medical Imaging 2019: Image Processing, vol. 10949, pp. 544\u2013549. SPIE (2019). https:\/\/doi.org\/10.1117\/12.2510967","DOI":"10.1117\/12.2510967"},{"issue":"2","key":"29_CR16","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1167\/tvst.9.2.29","volume":"9","author":"C Zheng","year":"2020","unstructured":"Zheng, C., et al.: Assessment of generative adversarial networks model for synthetic optical coherence tomography images of retinal disorders. Transl. Vision Sci. Technol. 9(2), 29 (2020). https:\/\/doi.org\/10.1167\/tvst.9.2.29","journal-title":"Transl. Vision Sci. Technol."},{"issue":"13","key":"29_CR17","doi-asserted-by":"publisher","first-page":"7393","DOI":"10.1007\/s00521-021-05826-w","volume":"33","author":"J Kugelman","year":"2021","unstructured":"Kugelman, J., Alonso-Caneiro, D., Read, S.A., Vincent, S.J., Chen, F.K., Collins, M.J.: Data augmentation for patch-based OCT chorio-retinal segmentation using generative adversarial networks. Neural Comput. Appl. 33(13), 7393\u20137408 (2021). https:\/\/doi.org\/10.1007\/s00521-021-05826-w","journal-title":"Neural Comput. Appl."},{"issue":"1","key":"29_CR18","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s40662-022-00277-3","volume":"9","author":"A You","year":"2022","unstructured":"You, A., Kim, J.K., Ryu, I.H., Yoo, T.K.: Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey. Eye Vision 9(1), 6 (2022). https:\/\/doi.org\/10.1186\/s40662-022-00277-3","journal-title":"Eye Vision"},{"key":"29_CR19","doi-asserted-by":"publisher","unstructured":"Hu, D., Tao, Y.K., Oguz, I.: Unsupervised denoising of retinal OCT with diffusion probabilistic model. In: Medical Imaging 2022: Image Processing, vol. 12032, pp. 25\u201334. SPIE (2022). https:\/\/doi.org\/10.1117\/12.2612235","DOI":"10.1117\/12.2612235"},{"key":"29_CR20","unstructured":"Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation (2018)"},{"issue":"12","key":"29_CR21","doi-asserted-by":"publisher","first-page":"4217","DOI":"10.1109\/TPAMI.2020.2970919","volume":"43","author":"T Karras","year":"2021","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. IEEE Trans. Pattern Anal. Mach. Intell. 43(12), 4217\u20134228 (2021). https:\/\/doi.org\/10.1109\/TPAMI.2020.2970919. Publisher: IEEE Computer Society","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"29_CR22","doi-asserted-by":"publisher","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA. IEEE (2020). https:\/\/doi.org\/10.1109\/CVPR42600.2020.00813. ISBN 978-1-72817-168-5","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"29_CR23","doi-asserted-by":"publisher","unstructured":"Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, pp. 1510\u20131519. IEEE (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.167","DOI":"10.1109\/ICCV.2017.167"},{"key":"29_CR24","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840\u20136851. Curran Associates, Inc. (2020)"},{"key":"29_CR25","doi-asserted-by":"publisher","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, pp. 10674\u201310685. IEEE (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.01042","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"29_CR26","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)"},{"key":"29_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"29_CR28","unstructured":"von Platen, P., et al.: Diffusers: state-of-the-art diffusion models (2024). https:\/\/github.com\/huggingface\/diffusers. Accessed 20 June 2024"}],"container-title":["Lecture Notes in Computer Science","Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-77731-8_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T16:46:37Z","timestamp":1732034797000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-77731-8_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,14]]},"ISBN":["9783031777301","9783031777318"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-77731-8_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,14]]},"assertion":[{"value":"14 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDEAL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Data Engineering and Automated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Valencia","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ideal2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}