{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:24:52Z","timestamp":1779384292003,"version":"3.53.1"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031721106","type":"print"},{"value":"9783031721113","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72111-3_16","type":"book-chapter","created":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T21:01:34Z","timestamp":1728162094000},"page":"166-176","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["DiffDGSS: Generalizable Retinal Image Segmentation with\u00a0Deterministic Representation from\u00a0Diffusion Models"],"prefix":"10.1007","author":[{"given":"Yingpeng","family":"Xie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junlong","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianfu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baiying","family":"Lei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,6]]},"reference":[{"key":"16_CR1","unstructured":"Baranchuk, D., Voynov, A., Rubachev, I., Khrulkov, V., Babenko, A.: Label-efficient semantic segmentation with diffusion models. In: International Conference on Learning Representations (2022)"},{"key":"16_CR2","doi-asserted-by":"publisher","first-page":"154860","DOI":"10.1155\/2013\/154860","volume":"2013","author":"A Budai","year":"2013","unstructured":"Budai, A., Bock, R., Maier, A., Hornegger, J., Michelson, G.: Robust vessel segmentation in fundus images. Int. J. Biomed. Imaging 2013, 154860 (2013)","journal-title":"Int. J. Biomed. Imaging"},{"key":"16_CR3","doi-asserted-by":"publisher","unstructured":"Carri\u00f3n, H., Norouzi, N.: Fedd-fair, efficient, and diverse diffusion-based lesion segmentation and malignancy classification. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 270\u2013279. Springer (2023). https:\/\/doi.org\/10.1007\/978-3-031-43990-2_26","DOI":"10.1007\/978-3-031-43990-2_26"},{"issue":"3","key":"16_CR4","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1177\/193229680900300315","volume":"3","author":"J Cuadros","year":"2009","unstructured":"Cuadros, J., Bresnick, G.: EyePACS: an adaptable telemedicine system for diabetic retinopathy screening. J. Diabetes Sci. Technol. 3(3), 509\u2013516 (2009)","journal-title":"J. Diabetes Sci. Technol."},{"key":"16_CR5","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. Adv. Neural. Inf. Process. Syst. 34, 8780\u20138794 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"9","key":"16_CR6","doi-asserted-by":"publisher","first-page":"2538","DOI":"10.1109\/TBME.2012.2205687","volume":"59","author":"MM Fraz","year":"2012","unstructured":"Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans. Biomed. Eng. 59(9), 2538\u20132548 (2012)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"11","key":"16_CR7","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"16_CR8","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"3","key":"16_CR9","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1109\/42.845178","volume":"19","author":"A Hoover","year":"2000","unstructured":"Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203\u2013210 (2000)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"16_CR10","unstructured":"Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)"},{"issue":"6","key":"16_CR11","doi-asserted-by":"publisher","first-page":"1596","DOI":"10.1109\/TMI.2022.3143833","volume":"41","author":"S Huang","year":"2022","unstructured":"Huang, S., Li, J., Xiao, Y., Shen, N., Xu, T.: RTNet: relation transformer network for diabetic retinopathy multi-lesion segmentation. IEEE Trans. Med. Imaging 41(6), 1596\u20131607 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"16_CR12","unstructured":"Hung, W.C., Tsai, Y.H., Liou, Y.T., Lin, Y.Y., Yang, M.H.: Adversarial learning for semi-supervised semantic segmentation. In: 29th British Machine Vision Conference, BMVC 2018 (2019)"},{"key":"16_CR13","unstructured":"Kwon, M., Jeong, J., Uh, Y.: Diffusion models already have a semantic latent space. In: International Conference on Learning Representations (2022)"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, J., Kreis, K., Torralba, A., Fidler, S.: Semantic segmentation with generative models: semi-supervised learning and strong out-of-domain generalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8300\u20138311 (2021)","DOI":"10.1109\/CVPR46437.2021.00820"},{"key":"16_CR15","unstructured":"Li, M., et al.: IPN-V2 and OCTA-500: methodology and dataset for retinal image segmentation. arXiv preprint arXiv:2012.07261 5, 16 (2020)"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Q., Chen, C., Qin, J., Dou, Q., Heng, P.A.: FedDG: federated domain generalization on medical image segmentation via episodic learning in continuous frequency space. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1013\u20131023 (2021)","DOI":"10.1109\/CVPR46437.2021.00107"},{"key":"16_CR17","unstructured":"Liu, Y., et al.: VMamba: visual state space model. arXiv preprint arXiv:2401.10166 (2024)"},{"issue":"12","key":"16_CR18","doi-asserted-by":"publisher","first-page":"3699","DOI":"10.1109\/TMI.2022.3193146","volume":"41","author":"J Lyu","year":"2022","unstructured":"Lyu, J., Zhang, Y., Huang, Y., Lin, L., Cheng, P., Tang, X.: AADG: automatic augmentation for domain generalization on retinal image segmentation. IEEE Trans. Med. Imaging 41(12), 3699\u20133711 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"16_CR19","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1109\/TMI.2020.3042802","volume":"40","author":"Y Ma","year":"2020","unstructured":"Ma, Y., et al.: Rose: a retinal oct-angiography vessel segmentation dataset and new model. IEEE Trans. Med. Imaging 40(3), 928\u2013939 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"Mokady, R., Hertz, A., Aberman, K., Pritch, Y., Cohen-Or, D.: Null-text inversion for editing real images using guided diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6038\u20136047 (2023)","DOI":"10.1109\/CVPR52729.2023.00585"},{"key":"16_CR21","unstructured":"Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning, pp. 8162\u20138171. PMLR (2021)"},{"issue":"10","key":"16_CR22","doi-asserted-by":"publisher","first-page":"2434","DOI":"10.1109\/TMI.2019.2906319","volume":"38","author":"C Playout","year":"2019","unstructured":"Playout, C., Duval, R., Cheriet, F.: A novel weakly supervised multitask architecture for retinal lesions segmentation on fundus images. IEEE Trans. Med. Imaging 38(10), 2434\u20132444 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"16_CR23","doi-asserted-by":"publisher","first-page":"101561","DOI":"10.1016\/j.media.2019.101561","volume":"59","author":"P Porwal","year":"2020","unstructured":"Porwal, P., et al.: IDRiD: diabetic retinopathy-segmentation and grading challenge. Med. Image Anal. 59, 101561 (2020)","journal-title":"Med. Image Anal."},{"key":"16_CR24","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: International Conference on Learning Representations (2020)"},{"issue":"4","key":"16_CR25","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"J Staal","year":"2004","unstructured":"Staal, J., Abr\u00e0moff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501\u2013509 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Wu, Y., He, K.: Group normalization. In: Proceedings of the European conference on computer vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01261-8_1"},{"issue":"3","key":"16_CR27","first-page":"3121","volume":"45","author":"W Xia","year":"2022","unstructured":"Xia, W., Zhang, Y., Yang, Y., Xue, J.H., Zhou, B., Yang, M.H.: Gan inversion: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 45(3), 3121\u20133138 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Yap, B.P., Ng, B.K.: Cut-paste consistency learning for semi-supervised lesion segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 6160\u20136169 (2023)","DOI":"10.1109\/WACV56688.2023.00610"},{"issue":"7","key":"16_CR29","doi-asserted-by":"publisher","first-page":"2531","DOI":"10.1109\/TMI.2020.2973595","volume":"39","author":"L Zhang","year":"2020","unstructured":"Zhang, L., et al.: Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation. IEEE Trans. Med. Imaging 39(7), 2531\u20132540 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: DatasetGAN: efficient labeled data factory with minimal human effort. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10145\u201310155 (2021)","DOI":"10.1109\/CVPR46437.2021.01001"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Zhou, Y., et al.: Collaborative learning of semi-supervised segmentation and classification for medical images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2079\u20132088 (2019)","DOI":"10.1109\/CVPR.2019.00218"},{"key":"16_CR32","first-page":"3833","volume":"33","author":"B Zoph","year":"2020","unstructured":"Zoph, B., et al.: Rethinking pre-training and self-training. Adv. Neural. Inf. Process. Syst. 33, 3833\u20133845 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72111-3_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,5]],"date-time":"2024-10-05T21:03:16Z","timestamp":1728162196000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72111-3_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721106","9783031721113"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72111-3_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"6 October 2024","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","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","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":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}