{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T20:55:54Z","timestamp":1776459354264,"version":"3.51.2"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:00:00Z","timestamp":1652227200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:00:00Z","timestamp":1652227200000},"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":["Wireless Pers Commun"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s11277-022-09728-5","type":"journal-article","created":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T18:04:15Z","timestamp":1652292255000},"page":"3641-3659","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dilated Deep Neural Architectures for Improving Retinal Vessel Extraction"],"prefix":"10.1007","volume":"125","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8732-8652","authenticated-orcid":false,"given":"V.","family":"Sathananthavathi","sequence":"first","affiliation":[]},{"given":"G.","family":"Indumathi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,5,11]]},"reference":[{"issue":"6","key":"9728_CR1","doi-asserted-by":"publisher","first-page":"1874","DOI":"10.1109\/JBHI.2014.2302749","volume":"18","author":"A Salazar-Gonzalez","year":"2014","unstructured":"Salazar-Gonzalez, A., Kaba, D., Li, Y., & Liu, X. (2014). Segmentation of the blood vessels and optic disk in retinal images. IEEE Journal of Biomedical and Health Informatics, 18(6), 1874\u20131886.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"4","key":"9728_CR2","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, I. A. (2018). Robust retinal vessel segmentation using vessel\u2019s location map and Frangi enhancement filter. IET Image Processing, 12(4), 494\u2013501.","journal-title":"IET Image Processing"},{"key":"9728_CR3","first-page":"3524","volume":"6","author":"TA Soomro","year":"2018","unstructured":"Soomro, T. A., Khan, T. M., Khan, M. A. U., et al. (2018). Impact of ICA-based image enhancement technique on retinal blood vessels segmentation. IET Image Processing, 6, 3524\u20133533.","journal-title":"IET Image Processing"},{"issue":"3","key":"9728_CR4","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. (2018). Robust retinal blood vessel segmentation using line detectors with multiple masks. IET Image Processing, 12(3), 389\u2013399.","journal-title":"IET Image Processing"},{"key":"9728_CR5","doi-asserted-by":"crossref","unstructured":"Fraz, M. M., et al. (2011). Retinal vessel extraction using first-order derivativeof gaussian and morphological processing. In Advances in Visual Computing. Springer, pp. 410\u2013420.","DOI":"10.1007\/978-3-642-24028-7_38"},{"key":"9728_CR6","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/260410","author":"Y Yin","year":"2013","unstructured":"Yin, Y., et al. (2013). Automatic segmentation and measurement of vasculature in retinal fundus images using probabilistic formulation. Computational and mathematical methods in medicine. https:\/\/doi.org\/10.1155\/2013\/260410.","journal-title":"Computational and mathematical methods in medicine"},{"key":"9728_CR7","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.compmedimag.2016.05.004","volume":"55","author":"C Zhu","year":"2017","unstructured":"Zhu, C., Zou, B., Zhao, R., Cui, J., Duan, X., Chen, Z., & Liang, Y. (2017). Retinal vessel segmentation in colour fundus images using extreme learning machine. Computerized Medical Imaging and Graphics, 55, 68\u201377.","journal-title":"Computerized Medical Imaging and Graphics"},{"issue":"1","key":"9728_CR8","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1109\/TMI.2010.2064333","volume":"30","author":"D Mar\u00edn","year":"2011","unstructured":"Mar\u00edn, D., Aquino, A., Geg\u00fandez-Arias, M. E., & Bravo, J. M. (2011). A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants based features. IEEE Transactions on Medical Imaging, 30(1), 146\u2013158.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"11","key":"9728_CR9","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. (2018). BAT algorithm inspired retinal blood vessel segmentation. IET Image Processing, 12(11), 2075\u20132083.","journal-title":"IET Image Processing"},{"issue":"5","key":"9728_CR10","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1049\/iet-ipr.2017.0284","volume":"12","author":"T Sumathi","year":"2018","unstructured":"Sumathi, T., Vivekanandan, P., & Balaji, R. (2018). Retinal vessel segmentation using neural network (RVSNN). Image Processing, 12(5), 669\u2013678.","journal-title":"Image Processing"},{"issue":"4","key":"9728_CR11","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer, E., Long, J., & Darrell, T. (2017). Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640\u2013651.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"3","key":"9728_CR12","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1007\/s11517-006-0141-2","volume":"45","author":"SA Salem","year":"2007","unstructured":"Salem, S. A., Salem, N. M., & Nandi, A. K. (2007). Segmentation of retinal blood vessel using a novel clustering algorithm (RACAL) with a partial supervision strategy. Medical & Biological Engineering & Computing, 45(3), 261\u2013273.","journal-title":"Medical & Biological Engineering & Computing"},{"key":"9728_CR13","doi-asserted-by":"publisher","first-page":"1455","DOI":"10.1002\/ima.22579","volume":"31","author":"V Sathananthavathi","year":"2021","unstructured":"Sathananthavathi, V., Indumathi, G., Mahiya, R., & Priyadarshini, S. (2021). Improvement of thin retinal vessel extraction using mean matting method. International Journal of Imaging Systems and Technology, 31, 1455\u20131467.","journal-title":"International Journal of Imaging Systems and Technology"},{"issue":"1","key":"9728_CR14","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/TBME.2016.2535311","volume":"64","author":"JI Orlando","year":"2017","unstructured":"Orlando, J. I., Prokofyeva, E., & Blaschko, M. B. (2017). A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images. IEEE Transactions on Biomedical Engineering, 64(1), 16\u201327.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"9728_CR15","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1016\/j.neucom.2014.07.059","volume":"149","author":"S Wang","year":"2015","unstructured":"Wang, S., Yin, Y., Cao, G., Wei, B., Zheng, Y., & Yang, G. (2015). Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing, 149, 708\u2013717.","journal-title":"Neurocomputing"},{"key":"9728_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2546227","author":"Pawe\u0142 Liskowski","year":"2016","unstructured":"Liskowski, Pawe\u0142, & Krawiec, Krzysztof. (2016). Segmenting retinal blood vessels with deepneural networks. IEEE Transactions on Medical Imaging. https:\/\/doi.org\/10.1109\/TMI.2016.2546227.","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"9728_CR17","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.patcog.2018.11.030","volume":"88","author":"Xiaohong Wang","year":"2019","unstructured":"Wang, Xiaohong, Jiang, Xudong, & Ren, Jianfeng. (2019). Blood vessel segmentation from fundus image by a cascade classification framework. Pattern Recognition, 88, 331\u2013341.","journal-title":"Pattern Recognition"},{"issue":"9","key":"9728_CR18","doi-asserted-by":"publisher","first-page":"1912","DOI":"10.1109\/TBME.2018.2828137","volume":"65","author":"Zengqiang Yan","year":"2018","unstructured":"Yan, Zengqiang, Yang, Xin, & Cheng, Kwang-Ting. (2018). Joint segment-level and pixel-wise losses for deep learning based retinal vessel segmentation. IEEE Transactions on Biomedical Engineering, 65(9), 1912\u20131923.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"9728_CR19","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.neucom.2018.05.011","volume":"309","author":"Hu Kai","year":"2018","unstructured":"Kai, Hu., Zhang, Zhenzhen, Niu, Xiaorui, Zhang, Yuan, Cao, Chunhong, Xiao, Fen, & Gao, Xieping. (2018). Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing, 309, 179\u2013191.","journal-title":"Neurocomputing"},{"key":"9728_CR20","doi-asserted-by":"crossref","unstructured":"Fu, H., Xu, Y., Kee\u00a0Wong, D. \u00a0W., & Liu, J. (2016). Retinal vessel segmentation via deep learning and conditional random field, In Proceedings MICCAI, pp. 132-139.","DOI":"10.1007\/978-3-319-46723-8_16"},{"key":"9728_CR21","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. (2018). Retinal blood vessel segmentation using fully convolutional network with transfer learning. Computerized Medical Imaging and Graphics, 68, 1\u201315.","journal-title":"Computerized Medical Imaging and Graphics."},{"key":"9728_CR22","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T., (2015). U-net Convolutional neural networks for biomedical image segmentation, In Proceeding MICCAI, pp. 234-241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"9728_CR23","doi-asserted-by":"crossref","unstructured":"Dasgupta, A., Singh, S. (2017) A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation, In Proceeding ISBI, pp. 18-21.","DOI":"10.1109\/ISBI.2017.7950512"},{"issue":"1","key":"9728_CR24","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/TMI.2015.2457891","volume":"35","author":"Qiaoliang Li","year":"2016","unstructured":"Li, Qiaoliang, Feng, Bowei, Xie, LinPei, Liang, Ping, Zhang, Huisheng, & Wang, Tianfu. (2016). A cross-modality learning approach forvessel segmentation in retinal images. IEEE Transactions on Medical Imaging, 35(1), 109\u2013118. https:\/\/doi.org\/10.1109\/TMI.2015.2457891.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"1","key":"9728_CR25","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.eswa.2018.06.034","volume":"112","author":"Americo Oliveira","year":"2018","unstructured":"Oliveira, Americo, Carlos, Sergio Pereira, & Silva, A. (2018). Retinal vessel segmentation based on fully convolutional neural networks. Expert Systems with Applications 112(1), 229\u2013242.","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"9728_CR26","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1007\/s10278-019-00250-y","volume":"33","author":"V Sathananthavathi","year":"2020","unstructured":"Sathananthavathi, V., & Indumathi, G. (2020). Parallel architecture of fully convolved neural network for retinal vessel segmentation. Journal of Digital Imaging, 33(1), 168\u2013180.","journal-title":"Journal of Digital Imaging"},{"issue":"2","key":"9728_CR27","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/j.bbe.2020.01.011","volume":"40","author":"C Tian","year":"2020","unstructured":"Tian, C., Fang, T., Fan, Y., & Wu, W. (2020). Multi-path convolutional neural network in fundus segmentation of blood vessels. Biocybernetics and Biomedical Engineering, 40(2), 583\u2013595.","journal-title":"Biocybernetics and Biomedical Engineering"},{"issue":"2","key":"9728_CR28","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.jestch.2020.07.008","volume":"24","author":"I Atli","year":"2021","unstructured":"Atli, I., & Gedik, O. S. (2021). Sine-Net: A fully convolutional deep learning architecture for retinal blood vessel segmentation. Engineering Science and Technology, an International Journal, 24(2), 271\u2013283.","journal-title":"Engineering Science and Technology, an International Journal"},{"key":"9728_CR29","doi-asserted-by":"publisher","first-page":"3505","DOI":"10.1007\/s11042-020-09372-w","volume":"80","author":"E Uysal","year":"2021","unstructured":"Uysal, E., & Guraksin, G. E. (2021). Computer-aided retinal vessel segmentation in retinal images: Convolutional neural networks. Multimedia Tools and Applications, 80, 3505\u20133528.","journal-title":"Multimedia Tools and Applications"},{"key":"9728_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-020-02008-y","author":"L Huang","year":"2020","unstructured":"Huang, L., & Liu, F. (2020). Retinal vessel segmentation using simple SPCNN model and line connector. The Visual Computer. https:\/\/doi.org\/10.1007\/s00371-020-02008-y.","journal-title":"The Visual Computer"},{"issue":"2","key":"9728_CR31","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1109\/JBHI.2018.2818620","volume":"23","author":"Marios","year":"2019","unstructured":"Marios, et al. (2019). Semantic segmentation of pathological lung tissue with dilated fullyconvolutional networks. IEEE Journal of Biomedical and Health Informatics, 23(2), 714\u2013722. https:\/\/doi.org\/10.1109\/JBHI.2018.2818620.","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"issue":"12","key":"9728_CR32","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"Vijay Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, Vijay. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transaction on Pattern Analysis and Machine Learning, 39(12), 2481\u20132495. https:\/\/doi.org\/10.1109\/TPAMI.2016.2644615.","journal-title":"IEEE Transaction on Pattern Analysis and Machine Learning"},{"key":"9728_CR33","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"JJ Staal","year":"2004","unstructured":"Staal, J. J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., & 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"},{"issue":"3","key":"9728_CR34","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 Transactions on Medical Imaging, 19(3), 203\u2013210.","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"1","key":"9728_CR35","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1109\/TMI.2010.2064333","volume":"30","author":"Diego Marin","year":"2011","unstructured":"Marin, Diego, Aquino, Arturo, Gegundez-Arias, Manuel Emilio, & Bravo, Jose Manuel. (2011). A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Transactions On Medical Imaging, 30(1), 146\u2013158. https:\/\/doi.org\/10.1109\/TMI.2010.2064333.","journal-title":"IEEE Transactions On Medical Imaging"},{"issue":"9","key":"9728_CR36","doi-asserted-by":"publisher","first-page":"2538","DOI":"10.1109\/TBME.2012.2205687","volume":"59","author":"MM Fraz","year":"2012","unstructured":"Fraz, M. M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A. R., Owen, C. G., & Barman, S. (2012). A, An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Transactions on Biomedical Engineering, 59(9), 2538\u20132548.","journal-title":"IEEE Transactions on Biomedical Engineering"}],"container-title":["Wireless Personal Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-022-09728-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11277-022-09728-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-022-09728-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,10]],"date-time":"2022-08-10T11:54:27Z","timestamp":1660132467000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11277-022-09728-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,11]]},"references-count":36,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["9728"],"URL":"https:\/\/doi.org\/10.1007\/s11277-022-09728-5","relation":{},"ISSN":["0929-6212","1572-834X"],"issn-type":[{"value":"0929-6212","type":"print"},{"value":"1572-834X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,11]]},"assertion":[{"value":"14 April 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors V. Sathananthavathi & G. Indumathi 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 performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}