{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T05:45:14Z","timestamp":1763963114392,"version":"3.45.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031766039"},{"type":"electronic","value":"9783031766046"}],"license":[{"start":{"date-parts":[[2024,11,17]],"date-time":"2024-11-17T00:00:00Z","timestamp":1731801600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,17]],"date-time":"2024-11-17T00:00:00Z","timestamp":1731801600000},"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\/978-3-031-76604-6_9","type":"book-chapter","created":{"date-parts":[[2024,11,16]],"date-time":"2024-11-16T00:16:50Z","timestamp":1731716210000},"page":"120-134","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["VAVnets: Retinal Vasculature Segmentation in\u00a0Few-Shot Scenarios"],"prefix":"10.1007","author":[{"given":"Idris","family":"Dulau","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benoit","family":"Recur","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Catherine","family":"Helmer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cecile","family":"Delcourt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marie","family":"Beurton-Aimar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,17]]},"reference":[{"key":"9_CR1","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, 501\u2013509 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR2","doi-asserted-by":"publisher","first-page":"57561","DOI":"10.1109\/ACCESS.2019.2914319","volume":"7","author":"S Zhang","year":"2019","unstructured":"Zhang, S., et al.: Simultaneous arteriole and venule segmentation of dual-modal fundus images using a multi-task cascade network. IEEE Access 7, 57561\u201357573 (2019)","journal-title":"IEEE Access"},{"key":"9_CR3","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1049\/iet-ipr.2012.0455","volume":"7","author":"J Odstrcil\u00edk","year":"2013","unstructured":"Odstrcil\u00edk, J., et al.: Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database. IET Image Process. 7, 373\u2013383 (2013)","journal-title":"IET Image Process."},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Orlando, J.I., Breda, J.B., van Keer, K., Blaschko, M.B., Blanco, P.J., Bulant, C.A.: Towards a glaucoma risk index based on simulated hemodynamics from fundus images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (2018)","DOI":"10.1007\/978-3-030-00934-2_8"},{"issue":"10","key":"9_CR5","doi-asserted-by":"publisher","first-page":"1292","DOI":"10.1007\/s10439-022-03058-0","volume":"50","author":"A Khandouzi","year":"2022","unstructured":"Khandouzi, A., Ariafar, A., Mashayekhpour, Z., Pazira, M., Baleghi, Y.: Retinal vessel segmentation, a review of classic and deep methods. Ann. Biomed. Eng. 50(10), 1292\u20131314 (2022)","journal-title":"Ann. Biomed. Eng."},{"key":"9_CR6","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":"9_CR7","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.821565","volume":"9","author":"J Xu","year":"2022","unstructured":"Xu, J., Shen, J., Wan, C., Jiang, Q., Yan, Z., Yang, W.: A few-shot learning-based retinal vessel segmentation method for assisting in the central serous chorioretinopathy laser surgery. Front. Med. 9, 821565 (2022)","journal-title":"Front. Med."},{"key":"9_CR8","doi-asserted-by":"publisher","first-page":"4902","DOI":"10.1109\/JBHI.2023.3298710","volume":"27","author":"H-C Shao","year":"2023","unstructured":"Shao, H.-C., Chen, C.-Y., Chang, M.-H., Yu, C.-H., Lin, C.-W., Yang, J.-W.: Retina-transnet: a gradient-guided few-shot retinal vessel segmentation net. IEEE J. Biomed. Health Inform. 27, 4902\u20134913 (2023)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"9_CR9","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, 3699\u20133711 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR10","unstructured":"Hu, D., Li, H., Liu, H., Yao, X., Wang, J., Oguz, I.: Vesselmorph: domain-generalized retinal vessel segmentation via shape-aware representation. arXiv preprint arXiv:2307.00240 (2023)"},{"key":"9_CR11","unstructured":"Hu, D., Li, H., Liu, H., Oguz, I.: Domain generalization for retinal vessel segmentation with vector field transformer. In: Proceedings of The 5th International Conference on Medical Imaging with Deep Learning (2022)"},{"key":"9_CR12","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.823436","volume":"9","author":"D Shi","year":"2022","unstructured":"Shi, D., et al.: A deep learning system for fully automated retinal vessel measurement in high throughput image analysis. Front. Cardiovasc. Med. 9, 823436 (2022)","journal-title":"Front. Cardiovasc. Med."},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Shi, D., He, S., Yang, J., Zheng, Y., He, M.: One-shot retinal artery and vein segmentation via cross-modality pretraining. Ophthalmol. Sci. 100363 (2023)","DOI":"10.1016\/j.xops.2023.100363"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Ruan, D., Wang, D., Zheng, Y., Zheng, N., Zheng, M.: Gaussian context transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp.\u00a015129\u201315138 (2021)","DOI":"10.1109\/CVPR46437.2021.01488"},{"key":"9_CR15","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp.\u00a0448\u2013456. PMLR (2015)"},{"issue":"1","key":"9_CR16","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"9_CR17","unstructured":"Eldan, R., Shamir, O.: The power of depth for feedforward neural networks. In: Conference on Learning Theory, pp.\u00a0907\u2013940. PMLR (2016)"},{"key":"9_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"9_CR19","unstructured":"Mnih, V.: Machine learning for aerial image labeling. Ph.D. thesis (2013)"},{"key":"9_CR20","unstructured":"K\u00f6nig, J., Jenkins, M., Mannion, M., Barrie, P., Morison, G.: What\u2019s cracking? a review and analysis of deep learning methods for structural crack segmentation, detection and quantification. arXiv preprint arXiv:2202.03714 (2022)"},{"key":"9_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101874","volume":"67","author":"L Mou","year":"2021","unstructured":"Mou, L., et al.: Cs2-Net: deep learning segmentation of curvilinear structures in medical imaging. Med. Image Anal. 67, 101874 (2021)","journal-title":"Med. Image Anal."},{"key":"9_CR22","doi-asserted-by":"crossref","unstructured":"Chalakkal, R.J., Abdulla, W.H., Sinumol, S.: Comparative analysis of university of Auckland diabetic retinopathy database. In: International Conference on Signal Processing Systems (2017)","DOI":"10.1145\/3163080.3163087"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Popovic, N., Vujosevic, S., Radunovi\u0107, M., Radunovi\u0107, M., Popovi\u0107, T.: Trend database: Retinal images of healthy young subjects visualized by a portable digital non-mydriatic fundus camera. PLoS ONE 16 (2021)","DOI":"10.1371\/journal.pone.0254918"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Jin, K., et al.: Fives: a fundus image dataset for artificial intelligence based vessel segmentation. Sci. Data 9 (2022)","DOI":"10.1038\/s41597-022-01564-3"},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.\u00a0770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["Lecture Notes in Computer Science","Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-76604-6_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T05:43:33Z","timestamp":1763963013000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-76604-6_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,17]]},"ISBN":["9783031766039","9783031766046"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-76604-6_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,17]]},"assertion":[{"value":"17 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CIARP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Iberoamerican Congress on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Talca","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chile","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":"26 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 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":"ciarp2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ciarp24.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}