{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T06:23:16Z","timestamp":1758349396751,"version":"3.44.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032049360"},{"type":"electronic","value":"9783032049377"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-04937-7_60","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:41:08Z","timestamp":1758260468000},"page":"631-641","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Robust Retinal Vessel Segmentation via\u00a0Reducing Open-Set Label Noises from\u00a0SAM-Generated Masks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7214-0569","authenticated-orcid":false,"given":"Minqing","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7585-374X","authenticated-orcid":false,"given":"Mengxian","family":"He","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9405-519X","authenticated-orcid":false,"given":"Wu","family":"Yuan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"60_CR1","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.artmed.2018.06.004","volume":"90","author":"S Akbar","year":"2018","unstructured":"Akbar, S., Akram, M.U., Sharif, M., Tariq, A., Khan, S.A.: Decision support system for detection of hypertensive retinopathy using arteriovenous ratio. Artif. Intell. Med. 90, 15\u201324 (2018)","journal-title":"Artif. Intell. Med."},{"key":"60_CR2","unstructured":"Chen, J., et al.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"60_CR3","doi-asserted-by":"crossref","unstructured":"Chen, T., et al.: Sam fails to segment anything? \u2013 sam-adapter: adapting sam in underperformed scenes: camouflage, shadow, and more (2023)","DOI":"10.1109\/ICCVW60793.2023.00361"},{"key":"60_CR4","doi-asserted-by":"crossref","unstructured":"Chen, X., Yuan, Y., Zeng, G., Wang, J.: Semi-supervised semantic segmentation with cross pseudo supervision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2613\u20132622 (2021)","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"60_CR5","doi-asserted-by":"publisher","first-page":"2552","DOI":"10.1109\/TIP.2019.2946078","volume":"29","author":"V Cherukuri","year":"2019","unstructured":"Cherukuri, V., Bg, V.K., Bala, R., Monga, V.: Deep retinal image segmentation with regularization under geometric priors. IEEE Trans. Image Process. 29, 2552\u20132567 (2019)","journal-title":"IEEE Trans. Image Process."},{"key":"60_CR6","unstructured":"Dai, L., et al.: A deep learning system for predicting time to progression of diabetic retinopathy. Nat. Med., 1\u201311 (2024)"},{"key":"60_CR7","unstructured":"Emma Dugas, Jared, Jorge, Will Cukierski: Eyepacs. https:\/\/kaggle.com\/competitions\/diabetic-retinopathy-detection (2015)"},{"key":"60_CR8","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"issue":"3","key":"60_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"},{"issue":"1","key":"60_CR10","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1038\/s41597-022-01564-3","volume":"9","author":"K Jin","year":"2022","unstructured":"Jin, K., et al.: Fives: a fundus image dataset for artificial intelligence based vessel segmentation. Scientific Data 9(1), 475 (2022)","journal-title":"Scientific Data"},{"key":"60_CR11","unstructured":"Kirillov, A., et al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)"},{"issue":"11","key":"60_CR12","doi-asserted-by":"publisher","first-page":"1784","DOI":"10.1016\/j.ophtha.2018.04.023","volume":"125","author":"R Klein","year":"2018","unstructured":"Klein, R., et al.: The relationship of retinal vessel geometric characteristics to the incidence and progression of diabetic retinopathy. Ophthalmology 125(11), 1784\u20131792 (2018)","journal-title":"Ophthalmology"},{"key":"60_CR13","unstructured":"Lee, D.H., et\u00a0al.: Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML. vol.\u00a03, p.\u00a0896. Atlanta (2013)"},{"issue":"12","key":"60_CR14","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"},{"key":"60_CR15","doi-asserted-by":"crossref","unstructured":"Ma, J., Wang, B.: Segment anything in medical images. arXiv preprint arXiv:2304.12306 (2023)","DOI":"10.1038\/s41467-024-44824-z"},{"key":"60_CR16","unstructured":"Mazumder, M., et al.: Dataperf: benchmarks for data-centric AI development. Adv. Neural Inf. Process. Syst. 36 (2024)"},{"key":"60_CR17","doi-asserted-by":"crossref","unstructured":"Qiao, S., Shen, W., Zhang, Z., Wang, B., Yuille, A.: Deep co-training for semi-supervised image recognition. In: Proceedings of the European Conference on Computer Vision (eccv), pp. 135\u2013152 (2018)","DOI":"10.1007\/978-3-030-01267-0_9"},{"key":"60_CR18","doi-asserted-by":"publisher","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","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"60_CR19","doi-asserted-by":"publisher","unstructured":"S\u00e1nchez, F.J., Bernal, J., S\u00e1nchez-Montes, C., de Miguel, C.R., Fern\u00e1ndez-Esparrach, G.: Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos. Mach. Vis. Appl. 28(8), 917\u2013936 (2017). https:\/\/doi.org\/10.1007\/s00138-017-0864-0","DOI":"10.1007\/s00138-017-0864-0"},{"issue":"9","key":"60_CR20","doi-asserted-by":"publisher","first-page":"2238","DOI":"10.1109\/TMI.2022.3161681","volume":"41","author":"Y Tan","year":"2022","unstructured":"Tan, Y., Yang, K.F., Zhao, S.X., Li, Y.J.: Retinal vessel segmentation with skeletal prior and contrastive loss. IEEE Trans. Med. Imaging 41(9), 2238\u20132251 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"60_CR21","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"60_CR22","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Semi-supervised semantic segmentation using unreliable pseudo-labels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4248\u20134257 (2022)","DOI":"10.1109\/CVPR52688.2022.00421"},{"issue":"11","key":"60_CR23","doi-asserted-by":"publisher","first-page":"3062","DOI":"10.1109\/TMI.2022.3176915","volume":"41","author":"Z Xu","year":"2022","unstructured":"Xu, Z., et al.: Anti-interference from noisy labels: mean-teacher-assisted confident learning for medical image segmentation. IEEE Trans. Med. Imaging 41(11), 3062\u20133073 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"60_CR24","doi-asserted-by":"crossref","unstructured":"Yang, L., Qi, L., Feng, L., Zhang, W., Shi, Y.: Revisiting weak-to-strong consistency in semi-supervised semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7236\u20137246 (2023)","DOI":"10.1109\/CVPR52729.2023.00699"},{"key":"60_CR25","doi-asserted-by":"publisher","unstructured":"Zhang, M., et al.: Characterizing label errors: confident learning for noisy-labeled image segmentation. In: Martel, A.L., et al., (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 721\u2013730. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_70","DOI":"10.1007\/978-3-030-59710-8_70"},{"key":"60_CR26","doi-asserted-by":"crossref","unstructured":"Zhou, T., Wang, W., Konukoglu, E., Van\u00a0Gool, L.: Rethinking semantic segmentation: a prototype view. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2582\u20132593 (2022)","DOI":"10.1109\/CVPR52688.2022.00261"},{"key":"60_CR27","doi-asserted-by":"publisher","unstructured":"Zhu, H., Shi, J., Wu, J.: Pick-and-learn: automatic quality evaluation for noisy-labeled image segmentation. In: Shen, D., et al., (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 576\u2013584. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_64","DOI":"10.1007\/978-3-030-32226-7_64"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04937-7_60","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:41:18Z","timestamp":1758260478000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04937-7_60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049360","9783032049377"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04937-7_60","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","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":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}