{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T04:22:51Z","timestamp":1776745371574,"version":"3.51.2"},"reference-count":39,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T00:00:00Z","timestamp":1631664000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61962054"],"award-info":[{"award-number":["61962054"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The the National Natural Science Foundation of China","award":["61163036"],"award-info":[{"award-number":["61163036"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Segmentation of retinal vessels is a critical step for the diagnosis of some fundus diseases. Methods: To further enhance the performance of vessel segmentation, we propose a method based on a gated skip-connection network with adaptive upsampling (GSAU-Net). In GSAU-Net, a novel skip-connection with gating is first utilized in the extension path, which facilitates the flow of information from the encoder to the decoder. Specifically, we used the gated skip-connection between the encoder and decoder to gate the lower-level information from the encoder. In the decoding phase, we used an adaptive upsampling to replace the bilinear interpolation, which recovers feature maps from the decoder to obtain the pixelwise prediction. Finally, we validated our method on the DRIVE, CHASE, and STARE datasets. Results: The experimental results showed that our proposed method outperformed some existing methods, such as DeepVessel, AG-Net, and IterNet, in terms of accuracy, F-measure, and AUCROC. The proposed method achieved a vessel segmentation F-measure of 83.13%, 81.40%, and 84.84% on the DRIVE, CHASE, and STARE datasets, respectively.<\/jats:p>","DOI":"10.3390\/s21186177","type":"journal-article","created":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T12:00:44Z","timestamp":1631707244000},"page":"6177","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Gated Skip-Connection Network with Adaptive Upsampling for Retinal Vessel Segmentation"],"prefix":"10.3390","volume":"21","author":[{"given":"Yun","family":"Jiang","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3184-9897","authenticated-orcid":false,"given":"Huixia","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6040-9113","authenticated-orcid":false,"given":"Shengxin","family":"Tao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102025","DOI":"10.1016\/j.media.2021.102025","article-title":"SCS-Net: A Scale and Context Sensitive Network for Retinal Vessel Segmentation","volume":"70","author":"Wu","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_2","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":"Chaudhuri","year":"1989","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7600","DOI":"10.1016\/j.eswa.2011.12.046","article-title":"Vessel segmentation and width estimation in retinal images using multiscale production of matched filter responses","volume":"39","author":"Li","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_4","first-page":"109","article-title":"Automated Detection of Retinal Blood Vessels in Diabetic Retinopathy Using Gabor Filter","volume":"4","author":"Jaspreet","year":"2012","journal-title":"Int. J. Comput. Sci. Netw. Secur."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"137024","DOI":"10.1155\/2015\/137024","article-title":"Segmentation of retinal blood vessels based on cake filter","volume":"2015","author":"Bao","year":"2015","journal-title":"BioMed Res. Int."},{"key":"ref_6","unstructured":"Salem, N.M., and Nandi, A.K. (2006, January 14\u201319). Segmentation of retinal blood vessels using scale-space features and K-nearest neighbour classifier. Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, Toulouse, France."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1109\/TITB.2010.2052282","article-title":"FABC: Retinal vessel segmentation using AdaBoost","volume":"14","author":"Domenico","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1109\/42.232264","article-title":"Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme","volume":"12","author":"Liu","year":"1993","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.compmedimag.2009.09.006","article-title":"Multi-scale retinal vessel segmentation using line tracking","volume":"34","author":"Vlachos","year":"2010","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1016\/j.compbiomed.2013.01.016","article-title":"A novel method for retinal vessel tracking using particle filters","volume":"43","author":"Nayebifar","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/42.700738","article-title":"A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering","volume":"17","author":"Tolias","year":"1998","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_12","unstructured":"Chutatape, O., Zheng, L., and Krishnan, S.M. (1998, January 1). Retinal blood vessel detection and tracking by atched Gaussian and Kalman filters. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286), Hong Kong, China."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Khalaf, A.F., Yassine, I.A., and Fahmy, A.S. (2016, January 25\u201328). Convolutional neural networks for deep feature learning in retinal vessel segmentation. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7532384"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fu, H., Xu, Y., Lin, S., Wong, D.W.K., and Liu, J. (2016, January 17\u201321). Deepvessel: Retinal vessel segmentation via deep learning and conditional random field. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Greece.","DOI":"10.1007\/978-3-319-46723-8_16"},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Wu, Y., Xia, Y., Song, Y., Zhang, D., Liu, D., Zhang, C., and Cai, W. (2019, January 13\u201317). Vessel-Net: Retinal vessel segmentation under multi-path supervision. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Shenzhen, China.","DOI":"10.1007\/978-3-030-32239-7_30"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.neucom.2018.10.098","article-title":"CcNet: A cross-connected convolutional network for segmenting retinal vessels using multiscale features","volume":"392","author":"Feng","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhang, S., Fu, H., Yan, Y., Zhang, Y., Wu, Q., Yang, M., Tan, M., and Xu, Y. (2019, January 13\u201317). Attention guided network for retinal image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Shenzhen, China.","DOI":"10.1007\/978-3-030-32239-7_88"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"2004","DOI":"10.1167\/iovs.08-3018","article-title":"Measuring retinal vessel tortuosity in 10-year-old children: Validation of the computer-assisted image analysis of the retina (CAIAR) program","volume":"50","author":"Owen","year":"2009","journal-title":"Investig. Ophthalmol. Visual Sci."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Krishnan, D., Taylor, G.W., and Fergus, R. (2010, January 13\u201318). Deconvolutional networks. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539957"},{"key":"ref_23","unstructured":"Dumoulin, V., and Visin, F. (2016). A guide to convolution arithmetic for deep learning. arXiv."},{"key":"ref_24","unstructured":"Zhuang, J. (2018). LadderNet: Multi-path networks based on U-Net for medical image segmentation. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Zhang, H., Tan, N., and Chen, L. (2019). Automatic retinal blood vessel segmentation based on fully convolutional neural networks. Symmetry, 11.","DOI":"10.3390\/sym11091112"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 14\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"Ce-net: Context encoder network for 2d medical image segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1007\/s00138-014-0638-x","article-title":"Discriminative vessel segmentation in retinal images by fusing context-aware hybrid features","volume":"25","author":"Cheng","year":"2014","journal-title":"Mach. Vis. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2181","DOI":"10.1007\/s11548-017-1619-0","article-title":"Multi-level deep supervised networks for retinal vessel segmentation","volume":"12","author":"Mo","year":"2017","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Hasan, M., Yakopcic, C., Taha, T.M., and Asari, V.K. (2018). Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv.","DOI":"10.1109\/NAECON.2018.8556686"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"76342","DOI":"10.1109\/ACCESS.2019.2922365","article-title":"Retinal vessels segmentation based on dilated multiscale convolutional neural network","volume":"7","author":"Jiang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"32826","DOI":"10.1109\/ACCESS.2020.2974027","article-title":"Attention Guided U-Net With Atrous Convolution for Accurate Retinal Vessels Segmentation","volume":"8","author":"Lv","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Yao, H., Wu, C., and Liu, W. (2021). A multiscale residual attention network for retinal vessel segmentation. Symmetry, 13.","DOI":"10.3390\/sym13101820"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hu, J., Wang, H., Wang, J., Wang, Y., He, F., and Zhang, J. (2021). SA-Net: A scale-attention network for medical image segmentation. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0247388"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Wu, C., Wang, G., Yao, H.X., and Liu, W.H. (2021). MFI-Net: A multi-resolution fusion input network for retinal vessel segmentation. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0253056"},{"key":"ref_37","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_38","doi-asserted-by":"crossref","unstructured":"Miao, Y.-C., and Cheng, Y. (2015, January 14\u201316). Automatic extraction of retinal blood vessel based on matched filtering and local entropy thresholding. Proceedings of the 2015 8th International Conference on Biomedical Engineering and Informatics (BMEI), Shenyang, China.","DOI":"10.1109\/BMEI.2015.7401474"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, L., Verma, M., Nakashima, Y., Nagahara, H., and Kawasaki, R. (2020, January 1\u20135). Iternet: Retinal image segmentation utilizing structural redundancy in vessel networks. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093621"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/18\/6177\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:59:58Z","timestamp":1760165998000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/18\/6177"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,15]]},"references-count":39,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["s21186177"],"URL":"https:\/\/doi.org\/10.3390\/s21186177","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,15]]}}}