{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T07:54:38Z","timestamp":1775894078585,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,7]],"date-time":"2018-11-07T00:00:00Z","timestamp":1541548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Fundus vessel analysis is a significant tool for evaluating the development of retinal diseases such as diabetic retinopathy and hypertension in clinical practice. Hence, automatic fundus vessel segmentation is essential and valuable for medical diagnosis in ophthalmopathy and will allow identification and extraction of relevant symmetric and asymmetric patterns. Further, due to the uniqueness of fundus vessel, it can be applied in the field of biometric identification. In this paper, we remold fundus vessel segmentation as a task of pixel-wise classification task, and propose a novel coarse-to-fine fully convolutional neural network (CF-FCN) to extract vessels from fundus images. Our CF-FCN is aimed at making full use of the original data information and making up for the coarse output of the neural network by harnessing the space relationship between pixels in fundus images. Accompanying with necessary pre-processing and post-processing operations, the efficacy and efficiency of our CF-FCN is corroborated through our experiments on DRIVE, STARE, HRF and CHASE DB1 datasets. It achieves sensitivity of 0.7941, specificity of 0.9870, accuracy of 0.9634 and Area Under Receiver Operating Characteristic Curve (AUC) of 0.9787 on DRIVE datasets, which surpasses the state-of-the-art approaches.<\/jats:p>","DOI":"10.3390\/sym10110607","type":"journal-article","created":{"date-parts":[[2018,11,7]],"date-time":"2018-11-07T10:32:07Z","timestamp":1541586727000},"page":"607","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["A Coarse-to-Fine Fully Convolutional Neural Network for Fundus Vessel Segmentation"],"prefix":"10.3390","volume":"10","author":[{"given":"Jianwei","family":"Lu","sequence":"first","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai 201804, China"},{"name":"Institute of Translational Medicine, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixuan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingle","family":"Chen","sequence":"additional","affiliation":[{"name":"High School Affiliated to Fudan University, Shanghai 200082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7052-7268","authenticated-orcid":false,"given":"Ye","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai 201804, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, J., Hu, Q., Imran, A., Zhang, L., Yang, J., and Wang, Q. (2018, January 23\u201327). Vessel Recognition of Retinal Fundus Images Based on Fully Convolutional Network. Proceedings of the 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), Tokyo, Japan.","DOI":"10.1109\/COMPSAC.2018.10268"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Poplin, R., Varadarajan, A.V., Blumer, K., Liu, Y., Mcconnell, M.V., Corrado, G.S., Peng, L., and Webster, D.R. (arXiv, 2017). Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning, arXiv.","DOI":"10.1038\/s41551-018-0195-0"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.cmpb.2012.03.009","article-title":"Blood vessel segmentation methodologies in retinal images\u2014A survey","volume":"108","author":"Fraz","year":"2012","journal-title":"Comput. Methods Prog. Biomed."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.media.2014.08.002","article-title":"Trainable COSFIRE filters for vessel delineation with application to retinal images","volume":"19","author":"Azzopardi","year":"2015","journal-title":"Med. Image Anal."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1109\/TPAMI.2003.1159954","article-title":"Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images","volume":"25","author":"Jiang","year":"2015","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.eswa.2017.02.015","article-title":"An unsupervised coarse-to-fine algorithm for blood vessel segmentation in fundus images","volume":"78","author":"Ramalho","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1049\/cje.2016.05.016","article-title":"An ensemble retinal vessel segmentation based on supervised learning in fundus images","volume":"25","author":"Zhu","year":"2016","journal-title":"Chin. J. Electron."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xiao, Z., Wang, M., Zhang, F., Geng, L., Wu, J., Su, L., and Tong, J. (2016, January 16\u201318). Retinal vessel segmentation based on adaptive difference of Gauss filter. Proceedings of the IEEE International Conference on Digital Signal Processing, Beijing, China.","DOI":"10.1109\/ICDSP.2016.7868506"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1109\/TMI.2004.825627","article-title":"Ridge-based vessel segmentation in color images of the retina","volume":"23","author":"Staal","year":"2004","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/42.845178","article-title":"Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response","volume":"19","author":"Hoover","year":"2000","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"154860","DOI":"10.1155\/2013\/154860","article-title":"Robust vessel segmentation in fundus images","volume":"2013","author":"Budai","year":"2013","journal-title":"Int. J. Biomed. Imaging"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1109\/TBME.2012.2205687","article-title":"An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation","volume":"59","author":"Fraz","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Singh, D., Dharmveer, S., and Singh, B. (2014, January 11\u201313). A new morphology based approach for blood vessel segmentation in retinal images. Proceedings of the 2014 Annual IEEE India Conference (INDICON), Pune, India.","DOI":"10.1109\/INDICON.2014.7030686"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.fcij.2017.10.001","article-title":"A thresholding based technique to extract retinal blood vessels from fundus images","volume":"2","author":"Dash","year":"2017","journal-title":"Future Comput. Inf. J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1049\/iet-ipr.2012.0455","article-title":"Retinal vessel segmentation by improved matched filtering: Evaluation on a new high-resolution fundus image database","volume":"7","author":"Odstrcilik","year":"2013","journal-title":"IET Image Process."},{"key":"ref_16","first-page":"191","article-title":"Automatic blood vessel segmentation in color images of retina","volume":"33","author":"Osareh","year":"2009","journal-title":"Iran. J. Sci. Technol. Trans. B"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.media.2016.05.004","article-title":"Brain Tumor Segmentation with Deep Neural Networks","volume":"35","author":"Havaei","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_18","first-page":"2852","article-title":"Deep neural networks segment neuronal membranes in electron microscopy images","volume":"25","author":"Dan","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1109\/TMI.2015.2457891","article-title":"A cross-modality learning approach for vessel segmentation in retinal images","volume":"35","author":"Li","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Song, J., and Boreom, L. (2017, January 11\u201315). Development of automatic retinal vessel segmentation method in fundus images via convolutional neural networks. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, Korea.","DOI":"10.1109\/EMBC.2017.8036916"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1109\/TMI.2016.2546227","article-title":"Segmenting retinal blood vessels with deep neural networks","volume":"35","author":"Liskowski","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fu, H., Xu, Y., Lin, S., Wong, D.W.K., and Liu, J. (2017, January 11\u201313). DeepVessel: Retinal Vessel Segmentation via Deep Learning and Conditional Random Field. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec City, QC, Canada.","DOI":"10.1007\/978-3-319-46723-8_16"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dasgupta, A., and Singh, S. (2017, January 18\u201321). A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. Proceedings of the ISBI, Melbourne, VIC, Australia.","DOI":"10.1109\/ISBI.2017.7950512"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, Q., Xie, L., Zhang, Q., Qi, S., Liang, P., Zhang, H., and Wang, T. (2015, January 14\u201316). A supervised method using convolutional neural networks for retinal vessel delineation. Proceedings of the 2015 8th International Congress on Image and Signal Processing (CISP), Shenyang, China.","DOI":"10.1109\/CISP.2015.7407916"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fu, H., Xu, Y., Wong, D.W.K., and Liu, J. (2016, January 13\u201316). Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech.","DOI":"10.1109\/ISBI.2016.7493362"},{"key":"ref_26","unstructured":"Yu, F., and Koltun, V. (arXiv, 2015). Multi-scale context aggregation by dilated convolutions, arXiv."},{"key":"ref_27","unstructured":"Simonyan, K., and Zisserman, A. (arXiv, 2014). Very deep convolutional networks for large-scale image recognition, arXiv."},{"key":"ref_28","first-page":"1451","article-title":"Understanding convolution for semantic segmentation","volume":"2018","author":"Wang","year":"2017","journal-title":"WACV"},{"key":"ref_29","unstructured":"Kingma, D.P., and Ba, J. (arXiv, 2014). Adam: A method for stochastic optimization, arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zheng, S., Jayasumana, S., Romeraparedes, B., Vineet, V., Su, Z., Du, D., Huang, C., and Torr, P.H.S. (2015, January 7\u201313). Conditional random fields as recurrent neural networks. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.179"},{"key":"ref_31","unstructured":"Lafferty, J.D., Mccallum, A., and Pereira, F.C.N. (July, January 28). Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. Proceedings of the Eighteenth International Conference on Machine Learning, Williamstown, MA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1109\/TMI.2006.879967","article-title":"Retinal vessel segmentation using the 2-D gabor wavelet and supervised classification","volume":"25","author":"Soares","year":"2006","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1049\/el.2017.2066","article-title":"Multi-level deep neural network for efficient segmentation of blood vessels in fundus images","volume":"53","author":"Ngo","year":"2017","journal-title":"Electron. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Niemeijer, M., Ginneken, B.V., and Loog, M. (2004, January 12). Comparative study of retinal vessel segmentation methods on a new publicly available database. Proceedings of the Medical Imaging 2004: Image Processing, San Diego, CA, USA.","DOI":"10.1117\/12.535349"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1016\/j.cmpb.2011.08.009","article-title":"An approach to localize the retinal blood vessels using bit planes and centerline detection","volume":"108","author":"Fraz","year":"2017","journal-title":"Comput. Meth. Prog. Biomed."},{"key":"ref_36","first-page":"325","article-title":"Multiscale approach for blood vessel segmentation on retinal fundus images","volume":"50","author":"Budai","year":"2009","journal-title":"Arvo Meet. Abst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_38","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (arXiv, 2017). Rethinking Atrous Convolution for Semantic Image Segmentation, arXiv."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/10\/11\/607\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:18:52Z","timestamp":1775261932000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/10\/11\/607"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,7]]},"references-count":38,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["sym10110607"],"URL":"https:\/\/doi.org\/10.3390\/sym10110607","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,7]]}}}