{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T14:02:19Z","timestamp":1790258539853,"version":"4.1.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032381880","type":"print"},{"value":"9783032381897","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"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":[[2027]]},"DOI":"10.1007\/978-3-032-38189-7_37","type":"book-chapter","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T13:28:25Z","timestamp":1790256505000},"page":"386-395","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["PhysOCT: Physics-Guided Retinal Modeling for\u00a0OCT Image Synthesis"],"prefix":"10.1007","author":[{"given":"Yutong","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhe","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"issue":"4","key":"37_CR1","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1109\/2944.796348","volume":"5","author":"JM Schmitt","year":"2002","unstructured":"Schmitt, J.M.: Optical coherence tomography (OCT): a review. IEEE J. Sel. Top. Quantum Electron. 5(4), 1205\u20131215 (2002)","journal-title":"IEEE J. Sel. Top. Quantum Electron."},{"issue":"4","key":"37_CR2","doi-asserted-by":"publisher","first-page":"2329","DOI":"10.1167\/iovs.13-13048","volume":"55","author":"L Zhang","year":"2014","unstructured":"Zhang, L., et al.: Quantifying disrupted outer retinal-subretinal layer in SD-OCT images in choroidal neovascularization. Investigat. Ophthalmol. Visual Sci. 55(4), 2329\u20132335 (2014)","journal-title":"Investigat. Ophthalmol. Visual Sci."},{"key":"37_CR3","doi-asserted-by":"crossref","unstructured":"Brand\u00e3o Fantozzi, C., et al.: A comparative study between clinical optical coherence tomography (OCT) analysis and artificial intelligence-based quantitative evaluation in the diagnosis of diabetic macular edema. Vision 9(3), 75 (2025)","DOI":"10.3390\/vision9030075"},{"key":"37_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106512","volume":"154","author":"M Moradi","year":"2023","unstructured":"Moradi, M., et al.: Deep ensemble learning for automated non-advanced AMD classification using optimized retinal layer segmentation and SD-OCT scans. Comput. Biol. Med. 154, 106512 (2023)","journal-title":"Comput. Biol. Med."},{"key":"37_CR5","unstructured":"Goodfellow, I.J.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014)"},{"key":"37_CR6","unstructured":"Sohl-Dickstein, J.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning (ICML) (2015)"},{"key":"37_CR7","unstructured":"Liu, X., Gong, C., Liu, Q.: Flow straight and fast: learning to generate and transfer data with rectified flow. In: International Conference on Learning Representations (ICLR) (2023)"},{"key":"37_CR8","unstructured":"Chambon, P.: RoentGen: vision-language foundation model for chest X-ray generation. arXiv preprint arXiv:2211.12737 (2022)"},{"key":"37_CR9","doi-asserted-by":"crossref","unstructured":"Zhan, C.: MedM2G: unifying medical multi-modal generation via cross-guided diffusion with visual invariant. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)","DOI":"10.1109\/CVPR52733.2024.01093"},{"key":"37_CR10","doi-asserted-by":"crossref","unstructured":"Konz, N.: Anatomically-controllable medical image generation with segmentation-guided diffusion models. In: Medical Image Computing and Computer Assisted Intervention - MICCAI (2024)","DOI":"10.1007\/978-3-031-72104-5_9"},{"key":"37_CR11","doi-asserted-by":"crossref","unstructured":"Li, S.: RetiDiff: diffusion-based synthesis of retinal OCT images for enhanced segmentation. In: Medical Image Computing and Computer Assisted Intervention - MICCAI (2025)","DOI":"10.1007\/978-3-032-04937-7_49"},{"key":"37_CR12","doi-asserted-by":"crossref","unstructured":"Wu, Y.: Retinal OCT synthesis with denoising diffusion probabilistic models for layer segmentation. arXiv preprint arXiv:2311.05479 (2023)","DOI":"10.1109\/ISBI56570.2024.10635836"},{"key":"37_CR13","volume-title":"The Finite Element Method","author":"OC Zienkiewicz","year":"2000","unstructured":"Zienkiewicz, O.C., Taylor, R.L.: The Finite Element Method, 5th edn. Butterworth-Heinemann, Oxford (2000)","edition":"5"},{"key":"37_CR14","doi-asserted-by":"crossref","unstructured":"Faure, F.: SOFA: a multi-model framework for interactive physical simulation. In: Soft Tissue Biomechanical Modeling for Computer Assisted Surgery. Springer, Cham (2012)","DOI":"10.1007\/8415_2012_125"},{"key":"37_CR15","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.jcp.2018.10.045","volume":"378","author":"M Raissi","year":"2019","unstructured":"Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686\u2013707 (2019)","journal-title":"J. Comput. Phys."},{"key":"37_CR16","doi-asserted-by":"crossref","unstructured":"Feng, Y.: PIE-NeRF: physics-based interactive elastodynamics with NeRF. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)","DOI":"10.1109\/CVPR52733.2024.00426"},{"key":"37_CR17","unstructured":"Li, X.: PAC-NeRF: physics augmented continuum neural radiance fields for geometry-agnostic system identification. In: International Conference on Learning Representations (ICLR) (2023)"},{"key":"37_CR18","doi-asserted-by":"crossref","unstructured":"Prabhushankar, M., Philbrick, K., Burlina, P.: OLIVES dataset: ophthalmic labels for investigating visual eye semantics. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 35, pp. 9201\u20139216 (2022)","DOI":"10.52202\/068431-0669"},{"key":"37_CR19","doi-asserted-by":"crossref","unstructured":"Huber, M., Binder, P., Frohmann, M.: Monitoring age-related macular degeneration progression in optical coherence tomography (MARIO), task 1-MICCAI challenge 2024. In: International Challenge on Device-Independent Diabetic Macular Edema Onset Prediction, pp. 154\u2013162. Springer, Cham (2024)","DOI":"10.1007\/978-3-031-86651-7_15"},{"key":"37_CR20","doi-asserted-by":"crossref","unstructured":"Xie, B.: OCT image generation for subretinal fluid segmentation with finite element structure modeling. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135. IEEE (2024)","DOI":"10.1109\/ISBI56570.2024.10635133"},{"issue":"10","key":"37_CR21","doi-asserted-by":"publisher","first-page":"3584","DOI":"10.1109\/TMI.2024.3384484","volume":"43","author":"K Huang","year":"2024","unstructured":"Huang, K., et al.: Diverse data generation for retinal layer segmentation with potential structure modeling. IEEE Trans. Med. Imaging 43(10), 3584\u20133595 (2024)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"37_CR22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0220571","volume":"14","author":"G Ciasca","year":"2019","unstructured":"Ciasca, G., et al.: Nanomechanical mapping helps explain differences in outcomes of eye microsurgery: a comparative study of macular pathologies. PLoS ONE 14(8), e0220571 (2019)","journal-title":"PLoS ONE"},{"key":"37_CR23","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Advances in Neural Information Processing Systems, vol. 33, pp. 6840\u20136851 (2020)"},{"key":"37_CR24","doi-asserted-by":"crossref","unstructured":"Rombach, R.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"37_CR25","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-38189-7_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T13:28:36Z","timestamp":1790256516000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-38189-7_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,25]]},"ISBN":["9783032381880","9783032381897"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-38189-7_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,25]]},"assertion":[{"value":"25 September 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2026\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}