{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T13:17:33Z","timestamp":1783430253980,"version":"3.54.6"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Key Project of Natural Science Foundation of Tianjin, China"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s11760-026-05532-x","type":"journal-article","created":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T19:34:02Z","timestamp":1782588842000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Age-related macular degeneration region segmentation for fundus images"],"prefix":"10.1007","volume":"20","author":[{"given":"Jun","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peilin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhitao","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Geng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanbei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,27]]},"reference":[{"issue":"2","key":"5532_CR1","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/S2214-109X(13)70145-1","volume":"2","author":"WL Wong","year":"2014","unstructured":"Wong, W.L., Su, X., Li, X., Cheung, C.M.G., Klein, R., Cheng, C.-Y., Wong, T.Y.: Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. Lancet Glob. Health 2(2), 106\u2013116 (2014)","journal-title":"Lancet Glob. Health"},{"issue":"23","key":"5532_CR2","doi-asserted-by":"publisher","first-page":"13053","DOI":"10.3390\/ijms252313053","volume":"25","author":"N Marchesi","year":"2024","unstructured":"Marchesi, N., Capierri, M., Pascale, A., Barbieri, A.: Different therapeutic approaches for dry and wet amd. Int. J. Mol. Sci. 25(23), 13053 (2024)","journal-title":"Int. J. Mol. Sci."},{"key":"5532_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102600","volume":"68","author":"S Das","year":"2021","unstructured":"Das, S., Kharbanda, K., Raman, R., Dhas, E.: Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy. Biomed. Signal Process. Control 68, 102600 (2021)","journal-title":"Biomed. Signal Process. Control"},{"key":"5532_CR4","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.ins.2019.06.011","volume":"501","author":"T Li","year":"2019","unstructured":"Li, T., Gao, Y., Wang, K., Guo, S., Liu, H., Kang, H.: Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Inf. Sci. 501, 511\u2013522 (2019)","journal-title":"Inf. Sci."},{"key":"5532_CR5","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation, pp. 234\u2013241. Springer (2015)"},{"key":"5532_CR6","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation, pp. 801\u2013818. (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"issue":"16","key":"5532_CR7","doi-asserted-by":"publisher","first-page":"5327","DOI":"10.3390\/s21165327","volume":"21","author":"MCS Tang","year":"2021","unstructured":"Tang, M.C.S., Teoh, S.S., Ibrahim, H., Embong, Z.: Neovascularization detection and localization in fundus images using deep learning. Sensors 21(16), 5327 (2021)","journal-title":"Sensors"},{"issue":"2","key":"5532_CR8","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1109\/TMI.2023.3317088","volume":"43","author":"Y Ling","year":"2023","unstructured":"Ling, Y., Wang, Y., Dai, W., Yu, J., Liang, P., Kong, D.: Mtanet: Multi-task attention network for automatic medical image segmentation and classification. IEEE Trans. Med. Imaging 43(2), 674\u2013685 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5532_CR9","doi-asserted-by":"crossref","unstructured":"Wang, H., Xie, S., Lin, L., Iwamoto, Y., Han, X.-H., Chen, Y.-W., Tong, R.: Mixed transformer u-net for medical image segmentation, pp. 2390\u20132394. IEEE (2022)","DOI":"10.1109\/ICASSP43922.2022.9746172"},{"issue":"4","key":"5532_CR10","first-page":"2826","volume":"35","author":"X Wang","year":"2021","unstructured":"Wang, X., Xu, M., Zhang, J., Jiang, L., Li, L.: Deep multi-task learning for diabetic retinopathy grading in fundus images 35(4), 2826\u20132834 (2021)","journal-title":"Deep multi-task learning for diabetic retinopathy grading in fundus images"},{"issue":"6","key":"5532_CR11","doi-asserted-by":"publisher","first-page":"1596","DOI":"10.1109\/TMI.2022.3143833","volume":"41","author":"S Huang","year":"2022","unstructured":"Huang, S., Li, J., Xiao, Y., Shen, N., Xu, T.: Rtnet: relation transformer network for diabetic retinopathy multi-lesion segmentation. IEEE Trans. Med. Imaging 41(6), 1596\u20131607 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5532_CR12","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.inffus.2021.02.017","volume":"73","author":"W He","year":"2021","unstructured":"He, W., Wang, X., Wang, L., Huang, Y., Yang, Z., Yao, X., Zhao, X., Ju, L., Wu, L., Wu, L.: Incremental learning for exudate and hemorrhage segmentation on fundus images. Information Fusion 73, 157\u2013164 (2021)","journal-title":"Information Fusion"},{"key":"5532_CR13","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation, pp. 1290\u20131299. (2022)","DOI":"10.1109\/CVPR52688.2022.00135"},{"issue":"10","key":"5532_CR14","doi-asserted-by":"publisher","first-page":"2828","DOI":"10.1109\/TMI.2022.3172773","volume":"41","author":"H Fang","year":"2022","unstructured":"Fang, H., Li, F., Fu, H., Sun, X., Cao, X., Lin, F., Son, J., Kim, S., Quellec, G., Matta, S.: Adam challenge: Detecting age-related macular degeneration from fundus images. IEEE Trans. Med. Imaging 41(10), 2828\u20132847 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"5532_CR15","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"5532_CR16","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"5532_CR17","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., Torr, P.H., et al.: Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers, 6881\u20136890 (2021)","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"5532_CR18","first-page":"12077","volume":"34","author":"E Xie","year":"2021","unstructured":"Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. Adv. Neural. Inf. Process. Syst. 34, 12077\u201312090 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"5532_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109512","volume":"253","author":"Z Han","year":"2022","unstructured":"Han, Z., Jian, M., Wang, G.-G.: Convunext: An efficient convolution neural network for medical image segmentation. Knowl.-Based Syst. 253, 109512 (2022)","journal-title":"Knowl.-Based Syst."},{"issue":"9","key":"5532_CR20","doi-asserted-by":"publisher","first-page":"2273","DOI":"10.1109\/TMI.2022.3162111","volume":"41","author":"J Song","year":"2022","unstructured":"Song, J., Chen, X., Zhu, Q., Shi, F., Xiang, D., Chen, Z., Fan, Y., Pan, L., Zhu, W.: Global and local feature reconstruction for medical image segmentation. IEEE Trans. Med. Imaging 41(9), 2273\u20132284 (2022)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05532-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-026-05532-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05532-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T12:48:50Z","timestamp":1783428530000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-026-05532-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,27]]},"references-count":20,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["5532"],"URL":"https:\/\/doi.org\/10.1007\/s11760-026-05532-x","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,27]]},"assertion":[{"value":"16 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 June 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"470"}}