{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:52:56Z","timestamp":1792788776003,"version":"4.5.0"},"reference-count":42,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T00:00:00Z","timestamp":1630972800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Xinjiang Uygur Autonomous Region","award":["2020D01C034"],"award-info":[{"award-number":["2020D01C034"]}]},{"name":"Tianshan Innovation Team of Xinjiang Uygur Autonomous Region","award":["2020D14044"],"award-info":[{"award-number":["2020D14044"]}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"crossref","award":["U1903213, 61771416 and 62041110"],"award-info":[{"award-number":["U1903213, 61771416 and 62041110"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Key R&D Program of China","award":["2018YFB1403202"],"award-info":[{"award-number":["2018YFB1403202"]}]},{"name":"Creative Research Groups of Higher Education of Xinjiang Uygur Au-tonomous Region","award":["XJEDU2017T002"],"award-info":[{"award-number":["XJEDU2017T002"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>The cardinal symptoms of some ophthalmic diseases observed through exceptional retinal blood vessels, such as retinal vein occlusion, diabetic retinopathy, etc. The advanced deep learning models used to obtain morphological and structural information of blood vessels automatically are conducive to the early treatment and initiative prevention of ophthalmic diseases. In our work, we propose a hierarchical dilation convolutional network (HDC-Net) to extract retinal vessels in a pixel-to-pixel manner. It utilizes the hierarchical dilation convolution (HDC) module to capture the fragile retinal blood vessels usually neglected by other methods. An improved residual dual efficient channel attention (RDECA) module can infer more delicate channel information to reinforce the discriminative capability of the model. The structured Dropblock can help our HDC-Net model to solve the network overfitting effectively. From a holistic perspective, the segmentation results obtained by HDC-Net are superior to other deep learning methods on three acknowledged datasets (DRIVE, CHASE-DB1, STARE), the sensitivity, specificity, accuracy, f1-score and AUC score are {0.8252, 0.9829, 0.9692, 0.8239, 0.9871}, {0.8227, 0.9853, 0.9745, 0.8113, 0.9884}, and {0.8369, 0.9866, 0.9751, 0.8385, 0.9913}, respectively. It surpasses most other advanced retinal vessel segmentation models. Qualitative and quantitative analysis demonstrates that HDC-Net can fulfill the task of retinal vessel segmentation efficiently and accurately.<\/jats:p>","DOI":"10.1371\/journal.pone.0257013","type":"journal-article","created":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T13:25:47Z","timestamp":1631021147000},"page":"e0257013","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":10,"title":["HDC-Net: A hierarchical dilation convolutional network for retinal vessel segmentation"],"prefix":"10.1371","volume":"16","author":[{"given":"Xiaolong","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0210-2273","authenticated-orcid":true,"given":"Liejun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuli","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"key":"pone.0257013.ref001","doi-asserted-by":"crossref","first-page":"954825","DOI":"10.1117\/12.2191281","article-title":"A study of red blood cell deformability in diabetic retinopathy using optical tweezers","volume":"vol. 9548","author":"TJ Smart","year":"2015","journal-title":"Optical trapping and optical micromanipulation XII"},{"issue":"8","key":"pone.0257013.ref002","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1016\/j.compmedimag.2009.06.003","article-title":"Algorithms for digital image processing in diabetic retinopathy","volume":"33","author":"RJ Winder","year":"2009","journal-title":"Comput Medical Imaging Graph"},{"issue":"2","key":"pone.0257013.ref003","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1161\/01.HYP.0000199104.61945.33","article-title":"Retinal vessel diameters and risk of hypertension: the Rotterdam Study","volume":"47","author":"MK Ikram","year":"2006","journal-title":"hypertension"},{"issue":"9","key":"pone.0257013.ref004","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/TMI.2006.879955","article-title":"Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction","volume":"25","author":"AM Mendonca","year":"2006","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"3","key":"pone.0257013.ref005","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/42.34715","article-title":"Detection of blood vessels in retinal images using two-dimensional matched filters","volume":"8","author":"S Chaudhuri","year":"1989","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"12","key":"pone.0257013.ref006","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1109\/TMI.2006.884190","article-title":"Retinal Vessel Centerline Extraction Using Multiscale Matched Filters, Confidence and Edge Measures","volume":"25","author":"M Sofka","year":"2006","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"4","key":"pone.0257013.ref007","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":"J Odstrcil\u00edk","year":"2013","journal-title":"IET Image Process"},{"issue":"2","key":"pone.0257013.ref008","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1109\/TITB.2009.2036604","article-title":"Parallel Multiscale Feature Extraction and Region Growing: Application in Retinal Blood Vessel Detection","volume":"14","author":"MA Palomera-P\u00e9rez","year":"2010","journal-title":"IEEE Transactions on Information Technology in Biomedicine"},{"issue":"7","key":"pone.0257013.ref009","doi-asserted-by":"crossref","first-page":"2437","DOI":"10.1016\/j.patcog.2014.01.006","article-title":"Retinal vessels segmentation based on level set and region growing","volume":"47","author":"YQ Zhao","year":"2014","journal-title":"Pattern Recognit"},{"issue":"5","key":"pone.0257013.ref010","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1109\/TBME.2010.2097599","article-title":"Retinal Image Analysis Using Curvelet Transform and Multistructure Elements Morphology by Reconstruction","volume":"58","author":"MS Miri","year":"2011","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"pone.0257013.ref011","doi-asserted-by":"crossref","unstructured":"Espona L, Carreira MJ, Penedo MG, Ortega M. Retinal vessel tree segmentation using a deformable contour model. In: 2008 19th International Conference on Pattern Recognition; 2008. pp. 1\u20134.","DOI":"10.1109\/ICPR.2008.4761762"},{"issue":"9","key":"pone.0257013.ref012","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.1109\/TMI.2009.2017941","article-title":"An Active Contour Model for Segmenting and Measuring Retinal Vessels","volume":"28","author":"B Al-Diri","year":"2009","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"9","key":"pone.0257013.ref013","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1109\/TMI.2015.2409024","article-title":"Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images","volume":"34","author":"Y Zhao","year":"2015","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"2","key":"pone.0257013.ref014","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.cmpb.2010.03.004","article-title":"Automatic model-based tracing algorithm for vessel segmentation and diameter estimation","volume":"100","author":"KK Delibasis","year":"2010","journal-title":"Comput Methods Programs Biomed"},{"issue":"6","key":"pone.0257013.ref015","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.compmedimag.2014.05.010","article-title":"A retinal vessel boundary tracking method based on Bayesian theory and multi-scale line detection","volume":"38","author":"J Zhang","year":"2014","journal-title":"Comput Medical Imaging Graph"},{"issue":"12","key":"pone.0257013.ref016","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"V Badrinarayanan","year":"2017","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"pone.0257013.ref017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Networks. In: Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States; 2012. pp. 1106\u20131114."},{"key":"pone.0257013.ref018","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015\u201418th International Conference Munich, Germany, October 5\u20139, 2015, Proceedings, Part III. vol. 9351 of Lecture Notes in Computer Science. Springer; 2015. pp. 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"pone.0257013.ref019","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2015. pp. 3431\u20133440.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"pone.0257013.ref020","doi-asserted-by":"crossref","unstructured":"Dasgupta A, Singh S. A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017); 2017. pp. 248\u2013251.","DOI":"10.1109\/ISBI.2017.7950512"},{"issue":"10","key":"pone.0257013.ref021","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1049\/iet-ipr.2015.0150","article-title":"Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement","volume":"9","author":"H Lidong","year":"2015","journal-title":"IET Image Process"},{"key":"pone.0257013.ref022","doi-asserted-by":"crossref","first-page":"47930","DOI":"10.1109\/ACCESS.2021.3068477","article-title":"Contrast Enhancement of Fundus Images by Employing Modified PSO for Improving the Performance of Deep Learning Models","volume":"9","author":"K Aurangzeb","year":"2021","journal-title":"IEEE Access"},{"key":"pone.0257013.ref023","article-title":"Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation","author":"MZ Alom","year":"2018","journal-title":"CoRR"},{"key":"pone.0257013.ref024","unstructured":"Ghiasi G, Lin T, Le QV. DropBlock: A regularization method for convolutional networks. In: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montr\u00e9al, Canada; 2018. pp. 10750\u201310760."},{"key":"pone.0257013.ref025","doi-asserted-by":"crossref","unstructured":"Guo C, Szemenyei M, Pei Y, Yi Y, Zhou W. SD-Unet: A Structured Dropout U-Net for Retinal Vessel Segmentation. In: 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE); 2019. pp. 439\u2013444.","DOI":"10.1109\/BIBE.2019.00085"},{"key":"pone.0257013.ref026","doi-asserted-by":"crossref","unstructured":"Wang B, Qiu S, He H. Dual Encoding U-Net for Retinal Vessel Segmentation. In: Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2019\u201422nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part I. vol. 11764 of Lecture Notes in Computer Science. Springer; 2019. pp. 84\u201392.","DOI":"10.1007\/978-3-030-32239-7_10"},{"key":"pone.0257013.ref027","doi-asserted-by":"crossref","unstructured":"Guo C, Szemenyei M, Yi Y, Wang W, Chen B, Fan C. SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation. In: 2020 25th International Conference on Pattern Recognition (ICPR); 2021. pp. 1236\u20131242.","DOI":"10.1109\/ICPR48806.2021.9413346"},{"key":"pone.0257013.ref028","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J, Kweon IS. CBAM: Convolutional Block Attention Module. In: Computer Vision\u2014ECCV 2018\u201415th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part VII. vol. 11211 of Lecture Notes in Computer Science. Springer; 2018. pp. 3\u201319.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"pone.0257013.ref029","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, et al. Dual Attention Network for Scene Segmentation. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2019. pp. 3141\u20133149.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"pone.0257013.ref030","doi-asserted-by":"crossref","unstructured":"Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2020. pp. 11531\u201311539.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"pone.0257013.ref031","doi-asserted-by":"crossref","unstructured":"Liu JJ, Hou Q, Cheng MM, Wang C, Feng J. Improving Convolutional Networks With Self-Calibrated Convolutions. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2020. pp. 10093\u201310102.","DOI":"10.1109\/CVPR42600.2020.01011"},{"key":"pone.0257013.ref032","unstructured":"Yu F, Koltun V. Multi-Scale Context Aggregation by Dilated Convolutions. In: Bengio Y, LeCun Y, editors. 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings; 2016."},{"key":"pone.0257013.ref033","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016. pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"4","key":"pone.0257013.ref034","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":"J Staal","year":"2004","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"9","key":"pone.0257013.ref035","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":"MM Fraz","year":"2012","journal-title":"IEEE Transactions on Biomedical Engineering"},{"issue":"3","key":"pone.0257013.ref036","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":"AD Hoover","year":"2000","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"pone.0257013.ref037","doi-asserted-by":"crossref","first-page":"165056","DOI":"10.1109\/ACCESS.2020.3022943","article-title":"An Unsupervised Retinal Vessel Segmentation Using Hessian and Intensity Based Approach","volume":"8","author":"M Alhussein","year":"2020","journal-title":"IEEE Access"},{"key":"pone.0257013.ref038","doi-asserted-by":"crossref","first-page":"105231","DOI":"10.1016\/j.cmpb.2019.105231","article-title":"A new robust method for blood vessel segmentation in retinal fundus images based on weighted line detector and hidden Markov model","volume":"187","author":"C Zhou","year":"2020","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"9","key":"pone.0257013.ref039","doi-asserted-by":"crossref","first-page":"1912","DOI":"10.1109\/TBME.2018.2828137","article-title":"Joint Segment-Level and Pixel-Wise Losses for Deep Learning Based Retinal Vessel Segmentation","volume":"65","author":"Z Yan","year":"2018","journal-title":"IEEE Trans Biomed Eng"},{"key":"pone.0257013.ref040","doi-asserted-by":"crossref","first-page":"105508","DOI":"10.1016\/j.cmpb.2020.105508","article-title":"A size-invariant convolutional network with dense connectivity applied to retinal vessel segmentation measured by a unique index","volume":"196","author":"Z Zhuo","year":"2020","journal-title":"Comput Methods Programs Biomed"},{"key":"pone.0257013.ref041","doi-asserted-by":"crossref","first-page":"131257","DOI":"10.1109\/ACCESS.2020.3008899","article-title":"Residual Connection-Based Encoder Decoder Network (RCED-Net) for Retinal Vessel Segmentation","volume":"8","author":"TM Khan","year":"2020","journal-title":"IEEE Access"},{"key":"pone.0257013.ref042","doi-asserted-by":"crossref","first-page":"e0247388","DOI":"10.1371\/journal.pone.0247388","article-title":"SA-Net: A scale-attention network for medical image segmentation","volume":"16","author":"J Hu","year":"2021","journal-title":"PloS one"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0257013","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T13:46:53Z","timestamp":1631022413000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0257013"}},"subtitle":[],"editor":[{"given":"Gulistan","family":"Raja","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,9,7]]},"references-count":42,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,9,7]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0257013","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,7]]}}}