{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T19:38:06Z","timestamp":1771702686190,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"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":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1007\/s11042-020-09372-w","type":"journal-article","created":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T22:05:52Z","timestamp":1600812352000},"page":"3505-3528","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Computer-aided retinal vessel segmentation in retinal images: convolutional neural networks"],"prefix":"10.1007","volume":"80","author":[{"given":"Esin","family":"Uysal","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1935-2781","authenticated-orcid":false,"given":"G\u00fcr Emre","family":"G\u00fcraksin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,22]]},"reference":[{"key":"9372_CR1","doi-asserted-by":"crossref","unstructured":"Bengio, Y (2009). Learning deep architectures for AI technical report 1312, Dept. IRO, Universit\u2019e de Montr\u2019eal, Montreal, Canada, 2: 1\u2013127.","DOI":"10.1561\/9781601982957"},{"key":"9372_CR2","doi-asserted-by":"publisher","first-page":"58791","DOI":"10.1109\/ACCESS.2019.2911892","volume":"7","author":"Y Chen","year":"2019","unstructured":"Chen Y, Wang J, Chen X, Zhu M, Yang K, Wang Z, Xia R (2019) Single-image super-resolution algorithm based on structural self-similarity and deformation block features. IEEE Access 7:58791\u201358801","journal-title":"IEEE Access"},{"key":"9372_CR3","doi-asserted-by":"publisher","unstructured":"Chen, Y, Wang, J, Liu, S, Chen, X, Xiong, J, Yang, K (2019). Multiscale fast correlation filtering tracking algorithm based on a feature fusion model. Concurrency Computat Pract Exper https:\/\/doi.org\/10.1002\/cpe.5533","DOI":"10.1002\/cpe.5533"},{"key":"9372_CR4","doi-asserted-by":"publisher","first-page":"4855","DOI":"10.1007\/s12652-018-01171-4","volume":"10","author":"Y Chen","year":"2019","unstructured":"Chen Y, Wang J, Xia R, Zhang Q, Cao Z, Yang K (2019) The visual object tracking algorithm research based on adaptive combination kernel. J Ambient Intell Human Comput 10:4855\u20134867","journal-title":"J Ambient Intell Human Comput"},{"key":"9372_CR5","doi-asserted-by":"crossref","unstructured":"David R (2014). Bull, chapter 4 - digital picture formats and representations, editor(s): David R. Bull, Communicating Pictures, Academic Press, 99\u2013132.","DOI":"10.1016\/B978-0-12-405906-1.00004-0"},{"key":"9372_CR6","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.cmpb.2010.03.004","volume":"100","author":"KK Delibasis","year":"2010","unstructured":"Delibasis KK, Kechriniotis AI, Tsonos C, Assimakis N (2010) Automatic model-based tracing algorithm for vessel segmentation and diameter estimation. Comput Methods Prog Biomed 100:108\u2013122","journal-title":"Comput Methods Prog Biomed"},{"key":"9372_CR7","doi-asserted-by":"crossref","unstructured":"Dodge, S, Karam, L, (2016). Understanding how image quality affects deep neural networks. Eighth international conference on quality of multimedia experience (QoMEX), pp. 1\u20136.","DOI":"10.1109\/QoMEX.2016.7498955"},{"key":"9372_CR8","first-page":"4558","volume":"2017","author":"D Fan","year":"2017","unstructured":"Fan D, Cheng M, Liu Y, Li T, Borji A (2017) Structure-measure: a new way to evaluate foreground maps, IEEE international conference on computer vision (ICCV). Venice 2017:4558\u20134567","journal-title":"Venice"},{"key":"9372_CR9","doi-asserted-by":"crossref","unstructured":"Fan, DP, Gong, C, Cao, Y, Ren, B, Cheng, MM, Borji, A, (2018). Enhanced-alignment measure for binary foreground map evaluation. Proceedings of the 27th international joint conference on artificial intelligence, 698-704.","DOI":"10.24963\/ijcai.2018\/97"},{"key":"9372_CR10","unstructured":"Fang, B, Hsu, W and Lee, MU (2003). On the detection of retinal vessels in fundus images. http:\/\/hdl.handle.net\/1721.1\/3675 (10.04.2019)."},{"key":"9372_CR11","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.bspc.2012.05.005","volume":"8","author":"A Fathi","year":"2012","unstructured":"Fathi A, Naghsh-Nilchi AR (2012) Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation. Biomedical Signal Processing and Control 8:71\u201380","journal-title":"Biomedical Signal Processing and Control"},{"key":"9372_CR12","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1016\/j.cmpb.2011.08.009","volume":"108","author":"MM Fraz","year":"2012","unstructured":"Fraz MM, Barman SA, Remagnino P, Hoppe A, Basit A, Uyyanonvara B, Rudnicka AR, Owen CG (2012) An approach to localize the retinal blood vessels using bit Planes and centerline detection. Comput Methods Prog Biomed 108:600\u2013616","journal-title":"Comput Methods Prog Biomed"},{"key":"9372_CR13","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.neucom.2019.04.062","volume":"356","author":"K Fu","year":"2019","unstructured":"Fu K, Zhao Q, Gu IYH, Yang J (2019) Deepside: a general deep framework for salient object detection. Neurocomputing 356:69\u201382","journal-title":"Neurocomputing"},{"key":"9372_CR14","doi-asserted-by":"publisher","first-page":"25221","DOI":"10.1007\/s11042-019-7719-9","volume":"78","author":"R Ghoshal","year":"2019","unstructured":"Ghoshal R, Saha A, Das S (2019) An improved vessel extraction scheme from retinal fundus images. Multimed Tools Appl 78:25221\u201325239","journal-title":"Multimed Tools Appl"},{"key":"9372_CR15","unstructured":"Glorot, X and Bengio, Y (2010). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the thirteenth international conference on artificial intelligence and statistics, Universite de Montr \u00b4 eal, Canada, 249\u2013256."},{"key":"9372_CR16","volume-title":"Deep learning","author":"IJ Goodfellow","year":"2017","unstructured":"Goodfellow IJ, Bengio Y, Courville A (2017) Deep learning. MIT Press, USA"},{"key":"9372_CR17","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1016\/j.measurement.2018.05.003","volume":"125","author":"Y Guo","year":"2018","unstructured":"Guo Y, Budak \u00dc, Vespa LJ, Khorasani E, \u015eeng\u00fcr A (2018) A retinal vessel detection approach using convolution neural network with reinforcement sample learning strategy. Measurement 125:586\u2013591","journal-title":"Measurement"},{"key":"9372_CR18","doi-asserted-by":"crossref","unstructured":"He, K, Zhang, X, Ren, S, Sun, J (2015). Delving deep into rectifiers: surpassing human-level performance on ImageNet classification, IEEE international conference on computer vision (ICCV), USA, December 07\u201313, 1026\u20131034.","DOI":"10.1109\/ICCV.2015.123"},{"key":"9372_CR19","unstructured":"Hemanth, DJ, Deperlioglu, O, Kose, U (2018). An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Computing and Applications,1\u201315."},{"issue":"3","key":"9372_CR20","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 (2000) Locating blood vessels in retinal images by piece-wise Threhsold probing of a matched filter response. IEEE Trans Med Imaging 19(3):203\u2013210","journal-title":"IEEE Trans Med Imaging"},{"key":"9372_CR21","unstructured":"https:\/\/cecas.clemson.edu\/~ahoover\/stare\/ 1.05.2020"},{"key":"9372_CR22","unstructured":"https:\/\/www.isi.uu.nl\/Research\/Databases\/DRIVE\/ 25.04.2019"},{"key":"9372_CR23","unstructured":"Kolar, R, Odstrcilik, J, Jan, J, Harabis, V (2011). Illumination correction and contrast equalization in colour fundus images. 19th European signal processing conference, Brno University of Technology, Barcelona, Spain, September 2, 298\u2013302."},{"key":"9372_CR24","first-page":"16977","volume":"5","author":"M Kumar","year":"2016","unstructured":"Kumar M, Rana A (2016) Image enhancement using contrast limited adaptive histogram equalization and wiener filter. International Journal Of Engineering And Computer Science 5:16977\u201316979","journal-title":"International Journal Of Engineering And Computer Science"},{"key":"9372_CR25","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proceedings of the IEEE 86:2278\u20132324","journal-title":"Proceedings of the IEEE"},{"issue":"5","key":"9372_CR26","doi-asserted-by":"publisher","first-page":"26","DOI":"10.3390\/jimaging5020026","volume":"2019","author":"HA Leopold","year":"2019","unstructured":"Leopold HA, Orchard J, Zelek JS, Lakshminarayanan V (2019) Pixelbnn: augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation. J Imaging 2019(5):26","journal-title":"J Imaging"},{"key":"9372_CR27","doi-asserted-by":"publisher","first-page":"2369","DOI":"10.1109\/TMI.2016.2546227","volume":"35","author":"P Liskowski","year":"2016","unstructured":"Liskowski P, Krawiec K (2016) Segmenting retinal blood vessels with newline deep neural networks. IEEE Trans Med Imaging 35:2369\u20132380","journal-title":"IEEE Trans Med Imaging"},{"key":"9372_CR28","first-page":"248","volume-title":"How to evaluate foreground maps","author":"R Margolin","year":"2014","unstructured":"Margolin R, Zelnik-Manor L, Tal A (2014) How to evaluate foreground maps. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, pp 248\u2013255"},{"key":"9372_CR29","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1109\/TMI.2010.2064333","volume":"30","author":"D Marin","year":"2011","unstructured":"Marin D, Aquino A, Geg\u00fandez-Arias ME, Bravo JM (2011) A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans Med Imaging 30:146\u2013158","journal-title":"IEEE Trans Med Imaging"},{"key":"9372_CR30","doi-asserted-by":"crossref","unstructured":"Melinscak, M, Prentasic, P, Loncaric S (2015). Retinal vessel segmentation using deep neural networks. VISAPP 2015- 10th international conference on computer vision theory and applications, Berlin, Germany,1: 577-582.","DOI":"10.5220\/0005313005770582"},{"key":"9372_CR31","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1109\/TMI.2006.879955","volume":"25","author":"AM Mendonca","year":"2006","unstructured":"Mendonca AM, ve Campilho A (2006) Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Med Imaging 25:1200\u20131213","journal-title":"IEEE Trans Med Imaging"},{"key":"9372_CR32","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.cmpb.2018.02.001","volume":"158","author":"S Moccia","year":"2018","unstructured":"Moccia S, Momi E, Hadji S, Mattos L (2018) Blood vessel segmentation algorithms \u2013 review of methods, data sets and evaluation metrics. Comput Methods Prog Biomed 158:71\u201391","journal-title":"Comput Methods Prog Biomed"},{"key":"9372_CR33","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1016\/j.patcog.2012.08.009","volume":"46","author":"UT Nguyen","year":"2013","unstructured":"Nguyen UT, Bhuiyan A, Park LA, Ramamohanarao K (2013) An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recogn 46:703\u2013715","journal-title":"Pattern Recogn"},{"key":"9372_CR34","doi-asserted-by":"crossref","unstructured":"Niemeijer, M, Staal, JJ, van Ginneken, B, Loog, M, Abramoff, MD, (2004). Comparative study of retinal vessel segmentation methods on a new publicly available database, in: SPIE Medical Imaging, Editor(s): J Michael Fitzpatrick, M Sonka, SPIE, vol. 5370, pp. 648\u2013656.","DOI":"10.1117\/12.535349"},{"key":"9372_CR35","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1109\/TMI.2007.898551","volume":"26","author":"E Ricci","year":"2007","unstructured":"Ricci E, Perfetti R (2007) Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans Med Imaging 26:1357\u20131365","journal-title":"IEEE Trans Med Imaging"},{"key":"9372_CR36","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s11517-006-0141-2","volume":"45","author":"SA Salem","year":"2007","unstructured":"Salem SA, Salem NM, Nandi AK (2007) Segmentation of retinal blood vessels using a novel clustering algorithm with a partial supervision strategy. Medical & Biological Engineering & Computing 45:261\u2013273","journal-title":"Medical & Biological Engineering & Computing"},{"key":"9372_CR37","doi-asserted-by":"crossref","unstructured":"Sane P and Agrawal R (2017). Pixel normalization from numeric data as input to neural networks for machine learning and image processing. IEEE WiSPNET conference, 2250\u20132254.","DOI":"10.1109\/WiSPNET.2017.8300154"},{"key":"9372_CR38","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1109\/TMI.2006.879967","volume":"25","author":"JV Soares","year":"2006","unstructured":"Soares JV, Leandro JJ, Cesar RM, Jelinek HF, Cree MJ (2006) Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Transaction of Medical Imaging 25:1214\u20131222","journal-title":"IEEE Transaction of Medical Imaging"},{"key":"9372_CR39","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1007\/s10044-017-0630-y","volume":"20","author":"TA Soomro","year":"2017","unstructured":"Soomro TA, Gao J, Khan TM, Hani AFM, Khan AUM, Manoranjan P (2017) Computerised approaches for the detection of diabetic retinopathy using retinal fundus images. Journal of Pattern Analysis and Application 20:927\u2013961","journal-title":"Journal of Pattern Analysis and Application"},{"key":"9372_CR40","doi-asserted-by":"crossref","unstructured":"Soomro, TA, Gao, J, Khan, MAU, Khan, TM, Paul, MA (2016). Role of image contrast enhancement technique for ophthalmologist as diagnostic tool for diabetic retinopathy. International conference on digital image computing: techniques and applications, Queensland, Australia.1- 8.","DOI":"10.1109\/DICTA.2016.7797078"},{"key":"9372_CR41","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"JJ Staal","year":"2004","unstructured":"Staal JJ, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B (2004) Ridge based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 23:501\u2013509","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"9372_CR42","doi-asserted-by":"publisher","first-page":"3231","DOI":"10.1001\/jama.1982.03320480047025","volume":"247","author":"EJ Sussman","year":"1982","unstructured":"Sussman EJ, Tsiaras WG, Soper KA (1982) Diagnosis of diabetic eye disease. J Am Med Assoc 247:3231\u20133234","journal-title":"J Am Med Assoc"},{"issue":"2","key":"9372_CR43","doi-asserted-by":"publisher","first-page":"168","DOI":"10.3390\/e21020168","volume":"21","author":"C Wang","year":"2019","unstructured":"Wang C, Zhao Z, Ren Q, Xu Y, Yu Y (2019) Dense U-net based on patch-based learning for retinal vessel segmentation. Entropy 21(2):168","journal-title":"Entropy"},{"key":"9372_CR44","doi-asserted-by":"publisher","first-page":"1724","DOI":"10.1097\/00004872-199512010-00039","volume":"13","author":"B Wasan","year":"1995","unstructured":"Wasan B, Cerutti A, Ford S, Marsh R (1995) Vascular network changes in the retina with age and hypertension. J Hypertens 13:1724\u20131728","journal-title":"J Hypertens"},{"key":"9372_CR45","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/S0039-6257(01)00234-X","volume":"46","author":"RKTY Wong","year":"2001","unstructured":"Wong RKTY, Klein BEK, Tielsch JM, Hubbard L, Nieto FJ (2001) Retinal microvascular abnormalities and their Reletionship with hypertension, cardiovascular disease, and mortality. Surv Ophthalmol 46:59\u201380","journal-title":"Surv Ophthalmol"},{"key":"9372_CR46","doi-asserted-by":"crossref","unstructured":"Yao, Z, Zhang, Z, Xu, LQ, (2016). Convolutional neural network for retinal blood vessel segmentation. In proceedings of the 9th international symposium on computational intelligence and design (ISCID), Hangzhou, China, 10\u201311 December 2016; pp. 406\u2013409.","DOI":"10.1109\/ISCID.2016.1100"},{"key":"9372_CR47","unstructured":"Yavuz, Z., (2018). Extraction of blood vessels with pixel based classification methods in retinal fundus images, Phd Thesis, Karadeniz Technical University, Institute of Science and Technology."},{"key":"9372_CR48","doi-asserted-by":"crossref","unstructured":"Yim, J, Sohn, KA, (2017). Enhancing the performance of convolutional neural networks on quality degraded data sets. arXiv:1710.06805.","DOI":"10.1109\/DICTA.2017.8227427"},{"key":"9372_CR49","doi-asserted-by":"publisher","first-page":"2314","DOI":"10.1016\/j.patcog.2011.01.007","volume":"44","author":"X You","year":"2011","unstructured":"You X, Peng Q, Yuan Y, Cheung Y, Lei J (2011) Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recogn 44:2314\u20132324","journal-title":"Pattern Recogn"},{"key":"9372_CR50","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 A (2010) Retinal vessel extraction by matched filter with first -order derivative of gaussian. Computersin Biologyand Medicine 40:438\u2013445","journal-title":"Computersin Biologyand Medicine"},{"key":"9372_CR51","first-page":"8779","volume-title":"International conference on computer vision (ICCV)","author":"JX Zhao","year":"2019","unstructured":"Zhao JX, Liu JJ, Fan DP, Cao Y, Yang J, Cheng MM (2019) EGNet: edge guidance network for salient object detection. In: International conference on computer vision (ICCV), pp 8779\u20138788"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09372-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-09372-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09372-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T00:08:38Z","timestamp":1632269318000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-09372-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,22]]},"references-count":51,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["9372"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09372-w","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,22]]},"assertion":[{"value":"30 September 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 July 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}