{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T18:36:55Z","timestamp":1773772615609,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,7,24]],"date-time":"2019-07-24T00:00:00Z","timestamp":1563926400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,7,24]],"date-time":"2019-07-24T00:00:00Z","timestamp":1563926400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging"],"published-print":{"date-parts":[[2020,2]]},"DOI":"10.1007\/s10278-019-00250-y","type":"journal-article","created":{"date-parts":[[2019,7,24]],"date-time":"2019-07-24T17:02:48Z","timestamp":1563987768000},"page":"168-180","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Parallel Architecture of Fully Convolved Neural Network for Retinal Vessel Segmentation"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8732-8652","authenticated-orcid":false,"given":"Sathananthavathi","family":".V","sequence":"first","affiliation":[]},{"given":"Indumathi","family":".G","sequence":"additional","affiliation":[]},{"given":"Swetha Ranjani","family":".A","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,7,24]]},"reference":[{"issue":"5","key":"250_CR1","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1109\/TITB.2010.2052282","volume":"14","author":"CA Lupas\u00b8cu","year":"2010","unstructured":"Lupas\u00b8cu CA, Tegolo D, Trucco E: FABC: retinal vessel segmentation using AdaBoost. IEEE Trans Inf Technol Biomed 14(5):1267\u20131274, 2010","journal-title":"IEEE Trans Inf Technol Biomed"},{"issue":"39","key":"250_CR2","first-page":"1","volume":"1","author":"B Dizdaroglu","year":"2014","unstructured":"Dizdaroglu B, Cansizoglu E a, Kalpathy-Cramer J, keck K, Chiang MF, Erdogmus D: Structure based level set method for automatic retinal vasculature segmentation. EURASIP J Image Video Process 1(39):1\u201326, 2014","journal-title":"EURASIP J Image Video Process"},{"issue":"113","key":"250_CR3","first-page":"1","volume":"1","author":"W Barkhoda","year":"2011","unstructured":"Barkhoda W, Akhlaqian F, Amiri MD, Nouroozzadeh MS: Retina identification based on the pattern of blood vessels using fuzzy logic. EURASIP J Adv Signal Process 1(113):1\u20138, 2011","journal-title":"EURASIP J Adv Signal Process"},{"key":"250_CR4","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.dsp.2018.06.006","volume":"81","author":"J Zhao","year":"2018","unstructured":"Zhao J, Yang J, Ai D, HongSong Y, Jiang YH, Luosh Z, Wang Y: Automatic retinal vessel segmentation using multi-scale superpixel chain tracking. Digital Signal Process 81:26\u201341, 2018","journal-title":"Digital Signal Process"},{"issue":"1","key":"250_CR5","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, Arias MEG, Bravo JM: 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(1):146\u2013158, 2011","journal-title":"IEEE Trans Med Imaging"},{"issue":"9","key":"250_CR6","doi-asserted-by":"publisher","first-page":"2538","DOI":"10.1109\/TBME.2012.2205687","volume":"59","author":"MM Fraz","year":"2012","unstructured":"Fraz MM, Remagnino P, Hoppe A, Uyyanonvara B, Rudnicka AR, Owen CG, Barman SA: 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"},{"key":"250_CR7","first-page":"112","volume-title":"Proceedings of the 33rd applied imagery pattern recognition workshop","author":"HA Firpi","year":"2004","unstructured":"Firpi HA, Goodman E: Swarmed feature selection. In: Proceedings of the 33rd applied imagery pattern recognition workshop. Washington: IEEE Computer Society, 2004, pp. 112\u2013118"},{"key":"250_CR8","doi-asserted-by":"crossref","unstructured":"Yang XS: Engineering Optimizations via Nature-Inspired Virtual Bee Algorithms. In: Mira J, \u00c1lvarez JR Eds. Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. IWINAC 2005. Lecture Notes in Computer Science, vol 3562. Springer, Berlin, Heidelberg","DOI":"10.1007\/11499305_33"},{"key":"250_CR9","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.neucom.2015.06.083","volume":"172","author":"EE Hossam","year":"2016","unstructured":"Hossam EE, Zawbaabc A bou M, Hassanien E: Binary grey wolf optimization approaches for feature selection. Neurocomputing 172:371\u2013381, 2016","journal-title":"Neurocomputing"},{"key":"250_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.eswa.2017.04.019","volume":"83","author":"C Yu-Peng","year":"2017","unstructured":"Yu-Peng C, Ying L, Gang W, Yue-Feng Z, Qian X, Jia-Hao F, Xue-Ting C: A novel bacterial foraging optimization algorithm for feature selection. Expert Syst Appl 83:1\u201317, 2017","journal-title":"Expert Syst Appl"},{"issue":"11","key":"250_CR11","doi-asserted-by":"publisher","first-page":"2075","DOI":"10.1049\/iet-ipr.2017.1266","volume":"12","author":"V Sathananthavathi","year":"2018","unstructured":"Sathananthavathi V, Indumathi G: BAT algorithm inspired retinal blood vessel segmentation. IET Image Process 12(11):2075\u20132083, 2018","journal-title":"IET Image Process"},{"key":"250_CR12","doi-asserted-by":"crossref","unstructured":"Fu H, Xu Y, Lin S, Kee Wong DW, Liu J: DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field. In: Ourselin S, Joskowicz L, Sabuncu M, Unal G, Wells W Eds. Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016. MICCAI 2016. Lecture Notes in Computer Science, vol 9901. Springer, Cham, 2016","DOI":"10.1007\/978-3-319-46723-8_16"},{"key":"250_CR13","doi-asserted-by":"crossref","unstructured":"Maninis KK, Pont-Tuset J, Arbel\u00e1ez P, Van Gool L: Deep Retinal Image Understanding. In: Ourselin S, Joskowicz L, Sabuncu M, Unal G, Wells W Eds. Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016. MICCAI 2016. Lecture Notes in Computer Science, vol9901. Springer, Cham, 2016","DOI":"10.1007\/978-3-319-46723-8_17"},{"issue":"229","key":"250_CR14","first-page":"242","volume":"112","author":"A Oliveira","year":"2018","unstructured":"Oliveira A, Pereira S, Silva CA: Retinal vessel segmentation based on fully convolutional neural networks. Expert Syst Appl 112(229):242, 2018","journal-title":"Expert Syst Appl"},{"key":"250_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compmedimag.2018.04.005","volume":"68","author":"Z Jiang","year":"2018","unstructured":"Jiang Z, Zhang H, Wang Y, Ko S-B: Retinal blood vessel segmentation using fully convolutional network with transfer learning. Comput Med Imaging Graph 68:1\u201315, 2018","journal-title":"Comput Med Imaging Graph"},{"key":"250_CR16","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 U, Vespa LJ, Khorasani E, \u015eengur A: A retinal vessel detection approach using convolution neural network with reinforcement sample learning strategy. Measurement 125:586\u2013591, 2018","journal-title":"Measurement"},{"key":"250_CR17","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: 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":"250_CR18","first-page":"648","volume":"5370","author":"M Niemeijer","year":"2004","unstructured":"Niemeijer M, Staal JJ, van Ginneken B, Loog M, Abramoff MD: Comparative study of retinal vessel segmentation methods on a new publicly available database. SPIE Med Imag 5370:648\u2013656, 2004","journal-title":"SPIE Med Imag"},{"issue":"3","key":"250_CR19","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 piece-wise threshold probing of a matched filter response. IEEE Trans Med Imaging 19(3):203\u2013210, 2000","journal-title":"IEEE Trans Med Imaging"},{"issue":"8","key":"250_CR20","doi-asserted-by":"publisher","first-page":"951","DOI":"10.1109\/TMI.2003.815900","volume":"22","author":"A Hoover","year":"2003","unstructured":"Hoover A, Goldbaum M: Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans Med Imaging 22(8):951\u2013958, 2003","journal-title":"IEEE Trans Med Imaging"},{"issue":"12","key":"250_CR21","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495, 2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"5","key":"250_CR22","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1016\/S0893-6080(03)00115-1","volume":"16","author":"M Matusugu","year":"2003","unstructured":"Matusugu M, Mori K, Mitari Y, Kaneda Y: Subject independent facial expression recognition with robust face detection using a convolutional neural network. Neural Netw 16(5):555\u2013559, 2003","journal-title":"Neural Netw"},{"key":"250_CR23","unstructured":"Ioffe S, Szegedy C: Batch normalization: accelerating deep network training by reducing internal covariate shift, 2015, arXiv:1807.01702."},{"key":"250_CR24","first-page":"1097","volume":"1","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE: Imagenet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst 1:1097\u20131105, 2012","journal-title":"Adv Neural Inf Proces Syst"},{"key":"250_CR25","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM: Pattern recognition and machine learning. New York: Springer, 2006"},{"issue":"11","key":"250_CR26","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: Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging 35(11):2369\u20132380, 2016","journal-title":"IEEE Trans Med Imaging"},{"key":"250_CR27","first-page":"474","volume-title":"Contrast limited adaptive histogram equalization. Graphic gems IV","author":"K Zuiderveld","year":"1994","unstructured":"Zuiderveld K: Contrast limited adaptive histogram equalization. Graphic gems IV. San Diego: Academic Press Professional, 1994, pp. 474\u2013485"},{"issue":"9","key":"250_CR28","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1109\/TMI.2006.879967","volume":"25","author":"JVB Soares","year":"2006","unstructured":"Soares JVB, Leandro JJG, Cesar RM, Jelinek HF, Cree MJ: Retinal vessel segmentation using the 2-D gabor wavelet and supervised classification. IEEE Trans Med Imaging 25(9):1214\u20131222, 2006","journal-title":"IEEE Trans Med Imaging"},{"issue":"9","key":"250_CR29","doi-asserted-by":"publisher","first-page":"2538","DOI":"10.1109\/TBME.2012.2205687","volume":"59","author":"MM Fraz","year":"2012","unstructured":"Fraz MM, Remagnino P, Hoppe 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":"7","key":"250_CR30","doi-asserted-by":"publisher","first-page":"1779","DOI":"10.1007\/s00138-014-0638-x","volume":"25","author":"E Cheng","year":"2014","unstructured":"Cheng E, Du L, Wu Y et al.: Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features. Mach Vis Appl 25(7):1779\u20131792, 2014","journal-title":"Mach Vis Appl"},{"key":"250_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bspc.2016.05.006","volume":"30","author":"S Aslani","year":"2016","unstructured":"Aslani S, Sarnel H: A new supervised retinal vessel segmentation method based on robust hybrid features. Biomed Signal Proc Control 30:1\u201312, 2016","journal-title":"Biomed Signal Proc Control"},{"issue":"4","key":"250_CR32","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1049\/iet-ipr.2017.0457","volume":"12","author":"M Shahid","year":"2018","unstructured":"Shahid M, Taj IA: Robust retinal vessel segmentation using vessel's location map and Frangi enhancement filter. IET Image Process 12(4):494\u2013501, 2018","journal-title":"IET Image Process"},{"key":"250_CR33","first-page":"3524","volume":"6","author":"TA Soomro","year":"2018","unstructured":"Soomro TA, Khan TM, Khan MAU et al.: Impact of ICA-based image enhancement technique on retinal blood vessels segmentation. IET Image Process 6:3524\u20133353, 2018","journal-title":"IET Image Process"},{"issue":"3","key":"250_CR34","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1049\/iet-ipr.2017.0329","volume":"12","author":"B Biswal","year":"2018","unstructured":"Biswal B, Pooja T, Bala Subrahmanyam N: Robust retinal blood vessel segmentation using line detectors with multiple masks. IET Image Process 12(3):389\u2013399, 2018","journal-title":"IET Image Process"},{"key":"250_CR35","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.patcog.2018.11.030","volume":"88","author":"X Wang","year":"2019","unstructured":"Wang X, Jiang X, Ren J: Blood vessel segmentation from fundus image by a cascade classification framework. Pattern Recogn 88:331\u2013341, 2019","journal-title":"Pattern Recogn"},{"issue":"1","key":"250_CR36","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/TMI.2015.2457891","volume":"35","author":"Q Li","year":"2016","unstructured":"Li Q, Feng B, Xie L, Liang P, Zhang H, Wang T: A cross-modality learning approach for vessel segmentation in retinal images. IEEE Trans Med Imaging 35(1):109\u2013118, 2016","journal-title":"IEEE Trans Med Imaging"},{"issue":"11","key":"250_CR37","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: Segmenting retinal blood vessels with deep neural networks. IEEE Trans Med Imaging 35(11):2369\u20132380, 2016","journal-title":"IEEE Trans Med Imaging"},{"key":"250_CR38","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.eswa.2018.06.034","volume":"112","author":"A Oliveira","year":"2018","unstructured":"Oliveira A, Pereira S, Silva CA: Retinal vessel segmentation based on fully convolutional neural networks. Expert Syst Appl 112:229\u2013242, 2018","journal-title":"Expert Syst Appl"},{"key":"250_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compmedimag.2018.04.005","volume":"68","author":"Z Jiang","year":"2018","unstructured":"Jiang Z, Zhang H, Wang Y, Ko SB: Retinal blood vessel segmentation using fully convolutional network with transfer learning. Comput Med Imaging Graph 68:1\u201315, 2018","journal-title":"Comput Med Imaging Graph"},{"issue":"9","key":"250_CR40","doi-asserted-by":"publisher","first-page":"1912","DOI":"10.1109\/TBME.2018.2828137","volume":"65","author":"Z Yan","year":"2018","unstructured":"Yan Z, Yang X, Cheng K-T: Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation. IEEE Trans Biomed Eng 65(9):1912\u20131923, 2018","journal-title":"IEEE Trans Biomed Eng"},{"key":"250_CR41","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.neucom.2018.05.011","volume":"309","author":"K Hu","year":"2018","unstructured":"Hu K, Zhang Z, Niu X, Zhang Y, Cao C, Xiao F, Gao X: Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing. 309:179\u2013191, 2018","journal-title":"Neurocomputing."},{"issue":"1","key":"250_CR42","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.media.2014.08.002","volume":"19","author":"G Azzopardi","year":"2015","unstructured":"Azzopardi G, Strisciuglio N, Vento M, Petkov N: Trainable cosfire filters for vessel delineation with application to retinal images. Med Image Anal 19(1):46\u201357, 2015","journal-title":"Med Image Anal"},{"issue":"3","key":"250_CR43","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: An effective retinal blood vessel segmentation method using multi scale line detection. Pattern Recogn 46(3):703\u2013715, 2013","journal-title":"Pattern Recogn"}],"container-title":["Journal of Digital Imaging"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-019-00250-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10278-019-00250-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-019-00250-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,7,22]],"date-time":"2020-07-22T23:24:09Z","timestamp":1595460249000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10278-019-00250-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,24]]},"references-count":43,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,2]]}},"alternative-id":["250"],"URL":"https:\/\/doi.org\/10.1007\/s10278-019-00250-y","relation":{},"ISSN":["0897-1889","1618-727X"],"issn-type":[{"value":"0897-1889","type":"print"},{"value":"1618-727X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,24]]},"assertion":[{"value":"24 July 2019","order":1,"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 that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}