{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T16:28:01Z","timestamp":1774628881762,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319467221","type":"print"},{"value":"9783319467238","type":"electronic"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-46723-8_17","type":"book-chapter","created":{"date-parts":[[2016,10,1]],"date-time":"2016-10-01T03:01:21Z","timestamp":1475290881000},"page":"140-148","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":297,"title":["Deep Retinal Image Understanding"],"prefix":"10.1007","author":[{"given":"Kevis-Kokitsi","family":"Maninis","sequence":"first","affiliation":[]},{"given":"Jordi","family":"Pont-Tuset","sequence":"additional","affiliation":[]},{"given":"Pablo","family":"Arbel\u00e1ez","sequence":"additional","affiliation":[]},{"given":"Luc","family":"Van Gool","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,10,2]]},"reference":[{"key":"17_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1007\/978-3-642-40811-3_66","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"C Becker","year":"2013","unstructured":"Becker, C., Rigamonti, R., Lepetit, V., Fua, P.: Supervised feature learning for curvilinear structure segmentation. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 526\u2013533. Springer, Heidelberg (2013). doi:10.1007\/978-3-642-40811-3_66"},{"issue":"3","key":"17_CR2","first-page":"243","volume":"43","author":"EJ Carmona","year":"2008","unstructured":"Carmona, E.J., Rinc\u00f3n, M., Garc\u00eda-Feijo\u00f3, J., Mart\u00ednez-de-la Casa, J.M.: Identification of the optic nerve head with genetic algorithms. AIIM 43(3), 243\u2013259 (2008)","journal-title":"AIIM"},{"issue":"6","key":"17_CR3","first-page":"1019","volume":"32","author":"J Cheng","year":"2013","unstructured":"Cheng, J., Liu, J., Xu, Y., Yin, F., Wong, D.W.K., Tan, N.M., Tao, D., Cheng, C.Y., Aung, T., Wong, T.Y.: Superpixel classification based optic disc and optic cup segmentation for glaucoma screening. IEEE T-MI 32(6), 1019\u20131032 (2013)","journal-title":"IEEE T-MI"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Doll\u00e1r, P., Zitnick, C.L.: Structured forests for fast edge detection. In: ICCV (2013)","DOI":"10.1109\/ICCV.2013.231"},{"issue":"1","key":"17_CR5","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.cmpb.2012.03.009","volume":"108","author":"MM Fraz","year":"2012","unstructured":"Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: Blood vessel segmentation methodologies in retinal images-a survey. Comput. Methods Programs Biomed. 108(1), 407\u2013433 (2012)","journal-title":"Comput. Methods Programs Biomed."},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Fumero, F., Alay\u00f3n, S., Sanchez, J., Sigut, J., Gonzalez-Hernandez, M.: Rim-one: an open retinal image database for optic nerve evaluation. In: CBMS, pp. 1\u20136 (2011)","DOI":"10.1109\/CBMS.2011.5999143"},{"key":"17_CR7","doi-asserted-by":"crossref","unstructured":"Ganin, Y., Lempitsky, V.: N$$^{4}$$-fields: neural network nearest neighbor fields for image transforms. In: ACCV (2014)","DOI":"10.1007\/978-3-319-16808-1_36"},{"issue":"1","key":"17_CR8","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","volume":"38","author":"R Girshick","year":"2016","unstructured":"Girshick, R., Donahue, J., Darrell, T., Malik, J.: Region-based convolutional networks for accurate object detection and segmentation. IEEE T-PAMI 38(1), 142\u2013158 (2016)","journal-title":"IEEE T-PAMI"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Gu, L., Cheng, L.: Learning to boost filamentary structure segmentation. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.80"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"Hariharan, B., Arbel\u00e1ez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298642"},{"issue":"3","key":"17_CR11","first-page":"203","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 T-MI 19(3), 203\u2013210 (2000)","journal-title":"IEEE T-MI"},{"key":"17_CR12","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)"},{"issue":"4","key":"17_CR13","first-page":"786","volume":"32","author":"S Morales","year":"2013","unstructured":"Morales, S., Naranjo, V., Angulo, J., Alca\u00f1iz, M.: Automatic detection of optic disc based on PCA and mathematical morphology. IEEE T-MI 32(4), 786\u2013796 (2013)","journal-title":"IEEE T-MI"},{"issue":"3","key":"17_CR14","first-page":"703","volume":"46","author":"UT Nguyen","year":"2013","unstructured":"Nguyen, U.T., Bhuiyan, A., Park, L.A., Ramamohanarao, K.: An effective retinal blood vessel segmentation method using multi-scale line detection. PR 46(3), 703\u2013715 (2013)","journal-title":"PR"},{"key":"17_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"634","DOI":"10.1007\/978-3-319-10404-1_79","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2014","author":"JI Orlando","year":"2014","unstructured":"Orlando, J.I., Blaschko, M.: Learning fully-connected CRFs for blood vessel segmentation in retinal images. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 634\u2013641. Springer, Heidelberg (2014). doi:10.1007\/978-3-319-10404-1_79"},{"issue":"7","key":"17_CR16","doi-asserted-by":"publisher","first-page":"1465","DOI":"10.1109\/TPAMI.2015.2481406","volume":"38","author":"J Pont-Tuset","year":"2015","unstructured":"Pont-Tuset, J., Marques, F.: Supervised evaluation of image segmentation and object proposal techniques. IEEE T-PAMI 38(7), 1465\u20131478 (2015)","journal-title":"IEEE T-PAMI"},{"issue":"10","key":"17_CR17","first-page":"1357","volume":"26","author":"E Ricci","year":"2007","unstructured":"Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE T-MI 26(10), 1357\u20131365 (2007)","journal-title":"IEEE T-MI"},{"key":"17_CR18","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Sironi, A., Lepetit, V., Fua, P.: Projection onto the manifold of elongated structures for accurate extraction. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.44"},{"issue":"9","key":"17_CR20","first-page":"1214","volume":"25","author":"JV Soares","year":"2006","unstructured":"Soares, J.V., Leandro, J.J., Cesar, R.M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE T-MI 25(9), 1214\u20131222 (2006)","journal-title":"IEEE T-MI"},{"issue":"4","key":"17_CR21","first-page":"501","volume":"23","author":"J Staal","year":"2004","unstructured":"Staal, J., Abr\u00e0moff, M.D., Niemeijer, M., Viergever, M., Van Ginneken, B., et al.: Ridge-based vessel segmentation in color images of the retina. IEEE T-MI 23(4), 501\u2013509 (2004)","journal-title":"IEEE T-MI"},{"key":"17_CR22","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"17_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1007\/3-540-45497-7_43","volume-title":"Medical Data Analysis","author":"T Walter","year":"2001","unstructured":"Walter, T., Klein, J.-C.: Segmentation of color fundus images of the human retina: detection of the optic disc and the vascular tree using morphological techniques. In: Crespo, J., Maojo, V., Martin, F. (eds.) ISMDA 2001. LNCS, vol. 2199, pp. 282\u2013287. Springer, Heidelberg (2001). doi:10.1007\/3-540-45497-7_43"},{"key":"17_CR24","doi-asserted-by":"crossref","unstructured":"Xie, S., Tu, Z.: Holistically-nested edge detection. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.164"},{"issue":"1","key":"17_CR25","first-page":"11","volume":"27","author":"A Youssif","year":"2008","unstructured":"Youssif, A., Ghalwash, A.Z., Ghoneim, A.: Optic disc detection from normalized digital fundus images by means of a vessels\u2019 direction matched filter. IEEE T-MI 27(1), 11\u201318 (2008)","journal-title":"IEEE T-MI"},{"issue":"4","key":"17_CR26","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1016\/j.compbiomed.2010.02.008","volume":"40","author":"B Zhang","year":"2010","unstructured":"Zhang, B., Zhang, L., Zhang, L., Karray, F.: Retinal vessel extraction by matched filter with first-order derivative of gaussian. Comput. Biol. Med. 40(4), 438\u2013445 (2010)","journal-title":"Comput. Biol. Med."},{"key":"17_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/978-3-319-24888-2_17","volume-title":"Machine Learning in Medical Imaging","author":"JG Zilly","year":"2015","unstructured":"Zilly, J.G., Buhmann, J.M., Mahapatra, D.: Boosting convolutional filters with entropy sampling for optic cup and disc image segmentation from fundus images. In: Zhou, L., Wang, L., Wang, Q., Shi, Y. (eds.) MLMI 2015. LNCS, vol. 9352, pp. 136\u2013143. Springer, Heidelberg (2015). doi:10.1007\/978-3-319-24888-2_17"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-46723-8_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T22:15:09Z","timestamp":1749593709000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-46723-8_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319467221","9783319467238"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-46723-8_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016]]},"assertion":[{"value":"2 October 2016","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"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":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2016","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2016","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 October 2016","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2016","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}