{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T19:22:56Z","timestamp":1770146576774,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T00:00:00Z","timestamp":1706140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Optical coherence tomography angiography (OCTA) offers critical insights into the retinal vascular system, yet its full potential is hindered by challenges in precise image segmentation. Current methodologies struggle with imaging artifacts and clarity issues, particularly under low-light conditions and when using various high-speed CMOS sensors. These challenges are particularly pronounced when diagnosing and classifying diseases such as branch vein occlusion (BVO). To address these issues, we have developed a novel network based on topological structure generation, which transitions from superficial to deep retinal layers to enhance OCTA segmentation accuracy. Our approach not only demonstrates improved performance through qualitative visual comparisons and quantitative metric analyses but also effectively mitigates artifacts caused by low-light OCTA, resulting in reduced noise and enhanced clarity of the images. Furthermore, our system introduces a structured methodology for classifying BVO diseases, bridging a critical gap in this field. The primary aim of these advancements is to elevate the quality of OCTA images and bolster the reliability of their segmentation. Initial evaluations suggest that our method holds promise for establishing robust, fine-grained standards in OCTA vascular segmentation and analysis.<\/jats:p>","DOI":"10.3390\/s24030774","type":"journal-article","created":{"date-parts":[[2024,1,25]],"date-time":"2024-01-25T08:44:07Z","timestamp":1706172247000},"page":"774","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Segmentation of Low-Light Optical Coherence Tomography Angiography Images under the Constraints of Vascular Network Topology"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8789-0151","authenticated-orcid":false,"given":"Zhi","family":"Li","sequence":"first","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3190-5669","authenticated-orcid":false,"given":"Gaopeng","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Binfeng","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Wenhao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Tianyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Zhaoyang","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Paediatrics, University of Cambridge, Cambridge CB2 1TN, UK"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9186-418X","authenticated-orcid":false,"given":"Kunyan","family":"Cai","sequence":"additional","affiliation":[{"name":"Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China"}]},{"given":"Tingyu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"}]},{"given":"Yaoqi","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"Lishui Institute, Hangzhou Dianzi University, Lishui 323000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4627-3392","authenticated-orcid":false,"given":"Yaqi","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Media Engineering, Communication University of Zhejiang, Hangzhou 310018, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4369-2417","authenticated-orcid":false,"given":"Kai","family":"Jin","sequence":"additional","affiliation":[{"name":"Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310027, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3971-8434","authenticated-orcid":false,"given":"Xingru","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China"},{"name":"School of Electronic Engineering and Computer Science, Queen Mary University of London, London E3 4BL, UK"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Akil, H., Huang, A.S., Francis, B.A., Sadda, S.R., and Chopra, V. (2017). Retinal vessel density from optical coherence tomography angiography to differentiate early glaucoma, pre-perimetric glaucoma and normal eyes. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0170476"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/TMI.2016.2593725","article-title":"Intensity and compactness enabled saliency estimation for leakage detection in diabetic and malarial retinopathy","volume":"36","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Carnevali, A., Mastropasqua, R., Gatti, V., Vaccaro, S., Mancini, A., D\u2019aloisio, R., Lupidi, M., Cerquaglia, A., Sacconi, R., and Borrelli, E. (2020). Optical coherence tomography angiography in intermediate and late age-related macular degeneration: Review of current technical aspects and applications. Appl. Sci., 10.","DOI":"10.3390\/app10248865"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Cuenca, I., Salobrar-Garc\u00eda, E., Gil-Salgado, I., S\u00e1nchez-Puebla, L., Elvira-Hurtado, L., Fern\u00e1ndez-Albarral, J.A., Ram\u00edrez-Tora\u00f1o, F., Barabash, A., de Frutos-Lucas, J., and Salazar, J.J. (2022). Characterization of Retinal Drusen in Subjects at High Genetic Risk of Developing Sporadic Alzheimer\u2019s Disease: An Exploratory Analysis. J. Pers. Med., 12.","DOI":"10.3390\/jpm12050847"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.parkreldis.2022.03.008","article-title":"Retinal microvascular impairment in Parkinson\u2019s disease with cognitive dysfunction","volume":"98","author":"Li","year":"2022","journal-title":"Park. Relat. Disord."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Cuenca, I., Salobrar-Garc\u00eda, E., Elvira-Hurtado, L., Fern\u00e1ndez-Albarral, J., S\u00e1nchez-Puebla, L., Salazar, J., Ram\u00edrez, J., Ram\u00edrez, A., and de Hoz, R. (2021). The Value of OCT and OCTA as Potential Biomarkers for Preclinical Alzheimer\u2019s Disease: A Review Study. Life, 11.","DOI":"10.3390\/life11070712"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"539","DOI":"10.3390\/healthcare10030539","article-title":"Differentiating Degenerative from Vascular Dementia with the Help of Optical Coherence Tomography Angiography Biomarkers","volume":"10","author":"Chalkias","year":"2022","journal-title":"Healthcare"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2515841420950508","DOI":"10.1177\/2515841420950508","article-title":"OCTA in neurodegenerative optic neuropathies: Emerging biomarkers at the eye\u2013brain interface","volume":"12","author":"Asanad","year":"2020","journal-title":"Ther. Adv. Ophthalmol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Cuenca, I., Salobrar-Garc\u00eda, E., S\u00e1nchez-Puebla, L., Espejel, E., Garc\u00eda del Arco, L., Rojas, P., Elvira-Hurtado, L., Fern\u00e1ndez-Albarral, J., Ram\u00edrez-Tora\u00f1o, F., and Barabash, A. (2022). Retinal Vascular Study Using OCTA in Subjects at High Genetic Risk of Developing Alzheimer\u2019s Disease and Cardiovascular Risk Factors. J. Clin. Med., 11.","DOI":"10.3390\/jcm11113248"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Prenta\u0161ic, P., Heisler, M., Mammo, Z., Lee, S., Merkur, A., Navajas, E., Beg, M.F., \u0160arunic, M., and Loncaric, S. (2016). Segmentation of the foveal microvasculature using deep learning networks. J. Biomed. Opt., 21.","DOI":"10.1117\/1.JBO.21.7.075008"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"101874","DOI":"10.1016\/j.media.2020.101874","article-title":"CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging","volume":"67","author":"Mou","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mou, L., Zhao, Y., Chen, L., Cheng, J., Gu, Z., Hao, H., Qi, H., Zheng, Y., Frangi, A., and Liu, J. (2019, January 13\u201317). CS-Net: Channel and spatial attention network for curvilinear structure segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Shenzhen, China.","DOI":"10.1007\/978-3-030-32239-7_80"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3343","DOI":"10.1109\/TMI.2020.2992244","article-title":"Image projection network: 3D to 2D image segmentation in OCTA images","volume":"39","author":"Li","year":"2020","journal-title":"IEEE Trans. Med. Imag."},{"key":"ref_15","unstructured":"Hu, D., Cui, C., Li, H., Larson, K.E., Tao, Y.K., and Oguz, I. (October, January 27). Life: A generalizable autodidactic pipeline for 3D OCT-A vessel segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Strasbourg, France."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yu, S., Xie, J., Hao, J., Zheng, Y., Zhang, J., Hu, Y., Liu, J., and Zhao, Y. (2021, January 13\u201316). 3D vessel reconstruction in OCT-angiography via depth map estimation. Proceedings of the 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France.","DOI":"10.1109\/ISBI48211.2021.9434042"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1167\/tvst.9.13.5","article-title":"Automated segmentation of optical coherence tomography angiography images: Benchmark data and clinically relevant metrics","volume":"9","author":"Giarratano","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"928","DOI":"10.1109\/TMI.2020.3042802","article-title":"Rose: A retinal oct-angiography vessel segmentation dataset and new model","volume":"40","author":"Ma","year":"2020","journal-title":"IEEE Trans. Med. Imag."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lee, Y.-C., and Yeung, L. (2021). Svs-net: A novel semantic segmentation network in optical coherence tomography angiography images. arXiv.","DOI":"10.1101\/2020.08.20.258905"},{"key":"ref_20","unstructured":"Chen, W., Wang, W., Yang, W., and Liu, J. (2018, January 3\u20136). Deep retinex decomposition for low-light enhancement. Proceedings of the British Machine Vision Conference, Newcastle, UK."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1007\/s11263-020-01407-x","article-title":"Beyond brightening low-light images","volume":"129","author":"Zhang","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, R., Zhang, Q., Fu, C.W., Shen, X., Zheng, W.S., and Jia, J. (2019, January 16\u201320). Underexposed photo enhancement using deep illumination estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00701"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yang, W., Wang, S., Fang, Y., Wang, Y., and Liu, J. (2020, January 13\u201319). From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00313"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2340","DOI":"10.1109\/TIP.2021.3051462","article-title":"Enlightengan: Deep light enhancement without paired supervision","volume":"30","author":"Jiang","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","unstructured":"Zhang, Y., Di, X., Zhang, B., and Wang, C. (2020). Self-supervised image enhancement network: Training with low light images only. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Guo, C., Li, C., Guo, J., Loy, C.C., Hou, J., Kwong, S., and Cong, R. (2020, January 14\u201319). Zero-reference deep curve estimation for low-light image enhancement. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Virtual.","DOI":"10.1109\/CVPR42600.2020.00185"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, R., Ma, L., Zhang, J., Fan, X., and Luo, Z. (2021, January 19\u201325). Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.01042"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1109\/TIP.2016.2639450","article-title":"Lime: Low-light image enhancement via illumination map estimation","volume":"26","author":"Guo","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"120812","DOI":"10.1016\/j.eswa.2023.120812","article-title":"GOMPS: Global Attention-Based Ophthalmic Image Measurement and Postoperative Appearance Prediction System","volume":"232","author":"Huang","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_30","unstructured":"Li, M., Huang, K., Xu, Q., Yang, J., Zhang, Y., Ji, Z., Xie, K., Yuan, S., Liu, Q., and Chen, Q. (2020). OCTA-500: A Retinal Dataset for Optical Coherence Tomography Angiography Study. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"014006","DOI":"10.1117\/1.JMI.6.1.014006","article-title":"Recurrent Residual U-Net for Medical Image Segmentation","volume":"6","author":"Alom","year":"2019","journal-title":"J. Med. Imaging"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","article-title":"Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images","volume":"53","author":"Schlemper","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wang, X., Yang, S., Tang, M., Wei, Y., Han, X., He, L., and Zhang, J. (2019, January 13). SK-Unet: An Improved U-net Model with Selective Kernel for the Segmentation of Multi-sequence Cardiac MR. Proceedings of the International Workshop on Statistical Atlases and Computational Models of the Heart, Shenzhen, China.","DOI":"10.1007\/978-3-030-39074-7_26"},{"key":"ref_35","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":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","unstructured":"Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (2016). ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 22\u201325). Pyramid Scene Parsing Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Peng, C., Zhang, X., Yu, G., Luo, G., and Sun, J. (2017, January 22\u201325). Large Kernel Matters\u2013Improve Semantic Segmentation by Global Convolutional Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.189"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018, January 20). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. Proceedings of the International Workshop on Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Granada, Spain.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.-W., and Wu, J. (2020, January 4\u20138). UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"108376","DOI":"10.1016\/j.agrformet.2021.108376","article-title":"Modeling the response of winter wheat phenology to low temperature stress at elongation and booting stages","volume":"303","author":"Xiao","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2361","DOI":"10.1038\/s41467-021-22324-8","article-title":"Intercalated architecture of MA2Z4 family layered van der Waals materials with emerging topological, magnetic and superconducting properties","volume":"12","author":"Wang","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"6625688","DOI":"10.1155\/2021\/6625688","article-title":"R2AU-Net: Attention Recurrent Residual Convolutional Neural Network for Multimodal Medical Image Segmentation","volume":"2021","author":"Zuo","year":"2021","journal-title":"Secur. Commun. Netw."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., and Barnard, K. (2021, January 3\u20138). Attentional Feature Fusion. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, WACV 2021, Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00360"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/774\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:48:53Z","timestamp":1760104133000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/3\/774"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,25]]},"references-count":45,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24030774"],"URL":"https:\/\/doi.org\/10.3390\/s24030774","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,25]]}}}