{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T10:07:36Z","timestamp":1777457256570,"version":"3.51.4"},"reference-count":37,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Heilongjiang Provincial Key R&D Programme Projects","award":["2023ZX06C15"],"award-info":[{"award-number":["2023ZX06C15"]}]},{"name":"Fundamental Research Funds for the Basic Scientific Research Program of Heilongjiang Provincial Universities","award":["2024-KYYWF-1082"],"award-info":[{"award-number":["2024-KYYWF-1082"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Big Data"],"published-print":{"date-parts":[[2026,2,1]]},"abstract":"<jats:p>Optical coherence tomography (OCT) offers significant advantages of noncontact operation, high resolution, and real-time imaging, making it particularly suitable for acquiring human retinal images and playing a crucial role in diagnosing and monitoring retinal diseases such as diabetic macular edema (DME). OCT is a key noninvasive imaging modality for retinal diseases such as DME, offering high-resolution visualization of retinal layers and fluid accumulations. However, retinal fluid segmentation faces several challenges including variations in fluid size, location, and shape, as well as complex irregular boundaries. To address these issues, we propose TL-TransUNet, a novel lightweight segmentation model based on TransUNet. The model incorporates a hybrid self-attention mechanism that effectively combines linear self-attention with residual filtered multilayer perceptron modules, reducing both parameter size and computational complexity while capturing global relationships and local details to improve segmentation performance for small lesions. Furthermore, the decoder employs wavelet convolution that utilizes wavelet transform to extract multi-scale features from low- to high-frequency components, enhancing the model\u2019s multi-scale learning capability. Experimental results on a public DME dataset demonstrate that our proposed method outperforms several mainstream segmentation approaches, demonstrating superior performance.<\/jats:p>","DOI":"10.1177\/2167647x261429851","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T14:05:22Z","timestamp":1774274722000},"page":"29-41","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["TL-TransUNet: An Improved Lightweight Semantic Segmentation Model of Macular Edema Lesions in Retinal OCT Images"],"prefix":"10.1177","volume":"14","author":[{"given":"Zhijun","family":"Gao","sequence":"first","affiliation":[{"name":"School of Computer Information and Engineering, Heilongjiang University of Science and Technology, Harbin, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yishuai","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Information and Engineering, Heilongjiang University of Science and Technology, Harbin, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinhuan","family":"Wang","sequence":"additional","affiliation":[{"name":"First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Yue","sequence":"additional","affiliation":[{"name":"School of Computer Information and Engineering, Heilongjiang University of Science and Technology, Harbin, China."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2026,3,23]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1957169"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2017.2695461"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.492670"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2012.2225152"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.8.001874"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1452-9"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","unstructured":"Long J Shelhamer E Darrell T. Fully convolutional networks for semantic segmentation. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE: Boston MA; 2015; pp. 3431\u20133440; doi: 10.1109\/CVPR.2015.7298965","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.02.011"},{"issue":"7","key":"e_1_3_3_11_2","first-page":"2416","article-title":"A deep learning based automatic drusen segmentation model for age related macular degeneration in ultra wide-field imaging","volume":"65","author":"Singh P","year":"2024","unstructured":"Singh P, , Kumar S, , Deitch I, et al. A deep learning based automatic drusen segmentation model for age related macular degeneration in ultra wide-field imaging. Invest Ophthalmol Vis Sci, 2024; 65(7):2416\u20132416.","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.05.002"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2983721"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.07.143"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3600335"},{"key":"e_1_3_3_16_2","unstructured":"Vaswani A Shazeer N Parmar N et al. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS\u201917 Curran Associates Inc.: Red Hook NY; 2017; pp. 6000\u20136010."},{"key":"e_1_3_3_17_2","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy A","year":"2020","unstructured":"Dosovitskiy A, , Beyer L, , Kolesnikov A, et al. An image is worth 16x16 words: Transformers for image recognition at scale. ArXiv, 2020; doi: 10.48550\/ARXIV.2010.11929","journal-title":"ArXiv"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103280"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","unstructured":"Liu Z Lin Y Cao Y et al. Swin transformer: Hierarchical vision transformer using shifted windows. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV). IEEE: Montreal QC; 2021; pp. 9992\u201310002; doi: 10.1109\/ICCV48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3230943"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2025.105670"},{"key":"e_1_3_3_23_2","doi-asserted-by":"publisher","unstructured":"Wang J Zhu W Wang P et al. Selective structured state-spaces for long-form video understanding. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE: Vancouver BC; 2023; pp. 6387\u20136397; doi: 10.1109\/CVPR52729.2023.00618","DOI":"10.1109\/CVPR52729.2023.00618"},{"key":"e_1_3_3_24_2","article-title":"U-Mamba: Enhancing long-range dependency for biomedical image segmentation","author":"Ma J","year":"2024","unstructured":"Ma J, , Li F, , Wang B. U-Mamba: Enhancing long-range dependency for biomedical image segmentation. ArXiv, 2024; doi: 10.48550\/ARXIV.2401.04722","journal-title":"ArXiv"},{"key":"e_1_3_3_25_2","article-title":"Mamba: Linear-time sequence modeling with selective state spaces","author":"Gu A","year":"2023","unstructured":"Gu A, , Dao T. Mamba: Linear-time sequence modeling with selective state spaces. ArXiv, 2023; doi: 10.48550\/ARXIV.2312.00752","journal-title":"ArXiv"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72111-3_54"},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2025.129447"},{"key":"e_1_3_3_28_2","first-page":"24261","volume-title":"Advances in Neural Information Processing Systems","author":"Tolstikhin IO","year":"2021","unstructured":"Tolstikhin IO, , Houlsby N, , Kolesnikov A, et al. MLP-mixer: An all-MLP architecture for vision. In: Advances in Neural Information Processing Systems. (Ranzato M, , Beygelzimer A, , Dauphin Y eds.) Curran Associates, Inc.; 2021; pp. 24261\u201324272."},{"key":"e_1_3_3_29_2","article-title":"Separable self-attention for mobile vision transformers","volume":"2023","author":"Mehta S","year":"2023","unstructured":"Mehta S, , Rastegari M. Separable self-attention for mobile vision transformers. Trans Mach Learn Res, 2023; 2023.","journal-title":"Trans Mach Learn Res"},{"key":"e_1_3_3_30_2","doi-asserted-by":"publisher","unstructured":"Zhou K Yu H Zhao WX et al. Filter-enhanced MLP is all you need for sequential recommendation. In: Proceedings of the ACM Web Conference 2022. ACM: Lyon France; 2022; pp. 2388\u20132399; doi: 10.1145\/3485447.3512111","DOI":"10.1145\/3485447.3512111"},{"key":"e_1_3_3_31_2","doi-asserted-by":"publisher","unstructured":"He K Zhang X Ren S et al. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2016; pp. 770\u2013778; doi: 10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","unstructured":"Finder SE Amoyal R Treister E et al. Wavelet convolutions for large receptive fields. In: Computer Vision \u2013 ECCV 2024: 18th European Conference Milan Italy September 29\u2013October 4 2024 Proceedings Part LIV. Springer-Verlag: Berlin Heidelberg; 2024; pp. 363\u2013380; doi: 10.1007\/978-3-031-72949-2_21","DOI":"10.1007\/978-3-031-72949-2_21"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1364\/BOE.5.003568"},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.5281\/ZENODO.15234385"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","unstructured":"Deng J Dong W Socher R et al. ImageNet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE: Miami FL; 2009; pp. 248\u2013255; doi: 10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_3_36_2","article-title":"SAM-Med2D","author":"Cheng J","year":"2023","unstructured":"Cheng J, , Ye J, , Deng Z, et al. SAM-Med2D. ArXiv, 2023; doi: 10.48550\/ARXIV.2308.16184","journal-title":"ArXiv"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.slast.2025.100265"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlastec.2023.109689"}],"container-title":["Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/2167647X261429851","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/2167647X261429851","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/2167647X261429851","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T13:04:05Z","timestamp":1777381445000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/2167647X261429851"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,1]]},"references-count":37,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,2,1]]}},"alternative-id":["10.1177\/2167647X261429851"],"URL":"https:\/\/doi.org\/10.1177\/2167647x261429851","relation":{},"ISSN":["2167-6461","2167-647X"],"issn-type":[{"value":"2167-6461","type":"print"},{"value":"2167-647X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,1]]}}}