{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T20:59:05Z","timestamp":1765486745184,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T00:00:00Z","timestamp":1650326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005392","name":"Xi'an University of Architecture and Technology","doi-asserted-by":"publisher","award":["1960320048"],"award-info":[{"award-number":["1960320048"]}],"id":[{"id":"10.13039\/501100005392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fundus is the only structure that can be observed without trauma to the human body. By analyzing color fundus images, the diagnosis basis for various diseases can be obtained. Recently, fundus image segmentation has witnessed vast progress with the development of deep learning. However, the improvement of segmentation accuracy comes with the complexity of deep models. As a result, these models show low inference speeds and high memory usages when deploying to mobile edges. To promote the deployment of deep fundus segmentation models to mobile devices, we aim to design a lightweight fundus segmentation network. Our observation comes from the fact that high-resolution representations could boost the segmentation of tiny fundus structures, and the classification of small fundus structures depends more on local features. To this end, we propose a lightweight segmentation model called LightEyes. We first design a high-resolution backbone network to learn high-resolution representations, so that the spatial relationship between feature maps can be always retained. Meanwhile, considering high-resolution features means high memory usage; for each layer, we use at most 16 convolutional filters to reduce memory usage and decrease training difficulty. LightEyes has been verified on three kinds of fundus segmentation tasks, including the hard exudate, the microaneurysm, and the vessel, on five publicly available datasets. Experimental results show that LightEyes achieves highly competitive segmentation accuracy and segmentation speed compared with state-of-the-art fundus segmentation models, while running at 1.6 images\/s Cambricon-1A speed and 51.3 images\/s GPU speed with only 36k parameters.<\/jats:p>","DOI":"10.3390\/s22093112","type":"journal-article","created":{"date-parts":[[2022,4,20]],"date-time":"2022-04-20T00:22:43Z","timestamp":1650414163000},"page":"3112","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["LightEyes: A Lightweight Fundus Segmentation Network for Mobile Edge Computing"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6841-753X","authenticated-orcid":false,"given":"Song","family":"Guo","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, Xi\u2019an University of Architecture and Technology, Xi\u2019an 710055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,19]]},"reference":[{"key":"ref_1","first-page":"332","article-title":"Developments in Non-Invasive Imaging to Guide Diagnosis and Treatment of Proliferative Diabetic Retinopathy: A Systematic Review","volume":"1","author":"Bowditch","year":"2021","journal-title":"Int. J. Transl. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.preteyeres.2018.07.004","article-title":"Artificial intelligence in retina","volume":"67","author":"Sadeghipour","year":"2018","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1161\/STROKEAHA.120.031886","article-title":"Retinal vasculature fractal and stroke mortality","volume":"52","author":"Liew","year":"2021","journal-title":"Stroke"},{"key":"ref_5","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xie, S., and Tu, Z. (2015, January 7\u201313). Holistically-nested edge detection. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.164"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.neucom.2019.04.019","article-title":"L-Seg: An end-to-end unified framework for multi-lesion segmentation of fundus images","volume":"349","author":"Guo","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Guo, S., Li, T., Wang, K., Zhang, C., and Kang, H. (2019, January 17\u201319). A Lightweight Neural Network for Hard Exudate Segmentation of Fundus Image. Proceedings of the International Conference on Artificial Neural Networks, Munich, Germany.","DOI":"10.1007\/978-3-030-30508-6_16"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Colomer, A., Igual, J., and Naranjo, V. (2020). Detection of early signs of diabetic retinopathy based on textural and morphological information in fundus images. Sensors, 20.","DOI":"10.3390\/s20041005"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1016\/j.media.2014.05.004","article-title":"Exudate detection in color retinal images for mass screening of diabetic retinopathy","volume":"18","author":"Zhang","year":"2014","journal-title":"Med. Image Anal."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Romero-Ora\u00e1, R., Garc\u00eda, M., Ora\u00e1-P\u00e9rez, J., L\u00f3pez-G\u00e1lvez, M.I., and Hornero, R. (2020). Effective fundus image decomposition for the detection of red lesions and hard exudates to aid in the diagnosis of diabetic retinopathy. Sensors, 20.","DOI":"10.3390\/s20226549"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.neucom.2018.02.035","article-title":"Exudate-based diabetic macular edema recognition in retinal images using cascaded deep residual networks","volume":"290","author":"Mo","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Huang, S., Li, J., Xiao, Y., Shen, N., and Xu, T. (2022). RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-lesion Segmentation. IEEE Trans. Med. Imaging.","DOI":"10.1109\/TMI.2022.3143833"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhou, Y., He, X., Huang, L., Liu, L., Zhu, F., Cui, S., and Shao, L. (2019, January 16\u201320). Collaborative learning of semi-supervised segmentation and classification for medical images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00218"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4571","DOI":"10.1049\/iet-ipr.2019.0804","article-title":"Microaneurysms segmentation and diabetic retinopathy detection by learning discriminative representations","volume":"14","author":"Sarhan","year":"2021","journal-title":"IET Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.eswa.2018.06.034","article-title":"Retinal vessel segmentation based on fully convolutional neural networks","volume":"112","author":"Oliveira","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wu, Y., Xia, Y., Song, Y., Zhang, Y., and Cai, W. (2018, January 16\u201320). Multiscale network followed network model for retinal vessel segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, Spain.","DOI":"10.1007\/978-3-030-00934-2_14"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Khawaja, A., Khan, T.M., Khan, M.A., and Nawaz, S.J. (2019). A multi-scale directional line detector for retinal vessel segmentation. Sensors, 19.","DOI":"10.3390\/s19224949"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.ijmedinf.2019.03.015","article-title":"BTS-DSN: Deeply supervised neural network with short connections for retinal vessel segmentation","volume":"126","author":"Guo","year":"2019","journal-title":"Int. J. Med. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.knosys.2019.04.025","article-title":"DUNet: A deformable network for retinal vessel segmentation","volume":"178","author":"Jin","year":"2019","journal-title":"Knowl.-Based Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3384","DOI":"10.1109\/JBHI.2020.3002985","article-title":"Hard attention net for automatic retinal vessel segmentation","volume":"24","author":"Wang","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106206","DOI":"10.1016\/j.cmpb.2021.106206","article-title":"A high resolution representation network with multi-path scale for retinal vessel segmentation","volume":"208","author":"Lin","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ooi, A.Z.H., Embong, Z., Abd Hamid, A.I., Zainon, R., Wang, S.L., Ng, T.F., Hamzah, R.A., Teoh, S.S., and Ibrahim, H. (2021). Interactive blood vessel segmentation from retinal fundus image based on canny edge detector. Sensors, 21.","DOI":"10.3390\/s21196380"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Yao, H., Tao, S., and Liang, J. (2021). Gated Skip-Connection Network with Adaptive Upsampling for Retinal Vessel Segmentation. Sensors, 21.","DOI":"10.3390\/s21186177"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Yao, H., Ma, Z., and Zhang, J. (2021). Bi-SANet\u2014Bilateral Network with Scale Attention for Retinal Vessel Segmentation. Symmetry, 13.","DOI":"10.3390\/sym13101820"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1109\/JBHI.2018.2872813","article-title":"A three-stage deep learning model for accurate retinal vessel segmentation","volume":"23","author":"Yan","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_30","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201322). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Maninis, K.K., Pont-Tuset, J., Arbel\u00e1ez, P., and Van Gool, L. (2016, January 17\u201321). Deep Retinal Image Understanding. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Greece.","DOI":"10.1007\/978-3-319-46723-8_17"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","article-title":"Deep high-resolution representation learning for visual recognition","volume":"43","author":"Wang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"104928","DOI":"10.1016\/j.compbiomed.2021.104928","article-title":"Fundus image segmentation via hierarchical feature learning","volume":"138","author":"Guo","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Guo, S., Li, T., Zhang, C., Li, N., Kang, H., and Wang, K. (2019, January 17\u201319). Random Drop Loss for Tiny Object Segmentation: Application to Lesion Segmentation in Fundus Images. Proceedings of the International Conference on Artificial Neural Networks, Munich, Germany.","DOI":"10.1007\/978-3-030-30508-6_18"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"101561","DOI":"10.1016\/j.media.2019.101561","article-title":"IDRiD: Diabetic Retinopathy\u2014Segmentation and Grading Challenge","volume":"59","author":"Porwal","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.irbm.2013.01.010","article-title":"TeleOphta: Machine learning and image processing methods for teleophthalmology","volume":"34","author":"Cazuguel","year":"2013","journal-title":"Irbm"},{"key":"ref_39","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_40","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_41","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 Programs Biomed."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional Architecture for Fast Feature Embedding. Proceedings of the ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_44","unstructured":"Glorot, X., and Bengio, Y. (2010, January 13\u201315). Understanding the difficulty of training deep feedforward neural networks. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Chia Laguna Resort, Sardinia, Italy."},{"key":"ref_45","unstructured":"Kingma, D.P., and Ba, J.L. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"231","DOI":"10.5566\/ias.1155","article-title":"Feedback on a publicly distributed image database: The Messidor database","volume":"33","author":"Zhang","year":"2014","journal-title":"Image Anal. Stereol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Almazroa, A., Alodhayb, S., Osman, E., Ramadan, E., Hummadi, M., Dlaim, M., Alkatee, M., Raahemifar, K., and Lakshminarayanan, V. (2018, January 6). Retinal fundus images for glaucoma analysis: The RIGA dataset. Proceedings of the Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, International Society for Optics and Photonics, Houston, TX, USA.","DOI":"10.1117\/12.2293584"},{"key":"ref_49","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"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3112\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:56:36Z","timestamp":1760136996000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/9\/3112"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,19]]},"references-count":49,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["s22093112"],"URL":"https:\/\/doi.org\/10.3390\/s22093112","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,4,19]]}}}