{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:23:07Z","timestamp":1772119387467,"version":"3.50.1"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T00:00:00Z","timestamp":1743811200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T00:00:00Z","timestamp":1743811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"This work was supported by the National Natural Science Foundation of China","award":["62266025"],"award-info":[{"award-number":["62266025"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s00530-025-01671-2","type":"journal-article","created":{"date-parts":[[2025,4,5]],"date-time":"2025-04-05T22:17:52Z","timestamp":1743891472000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Tubular-aware mamba for accurate retinal vessel segmentation: preserving fine details and topological connectivity"],"prefix":"10.1007","volume":"31","author":[{"given":"Dangguo","family":"Shao","sequence":"first","affiliation":[]},{"given":"Rui","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Sanli","family":"Yi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,5]]},"reference":[{"key":"1671_CR1","doi-asserted-by":"crossref","unstructured":"Joes Staal, M.D., Abr`amoff, M., Niemeijer, M.A., Viergever: Ridgebased vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging. 23(4), 501\u2013509 (2004). Van","DOI":"10.1109\/TMI.2004.825627"},{"key":"1671_CR2","doi-asserted-by":"crossref","unstructured":"Christopher, G., Owen, A.R., Rudnicka, R., Mullen, S.A., Barman, D., Monekosso, Peter, H., Whincup, J., Ng, Carl Paterson: Measuring retinal vessel tortuosity in 10-year-old children: Validation of the computer-assisted image analysis of the retina (caiar) program. Investig. Ophthalmol. Vis. Sci. 50(5), 2004\u20132010 (2009)","DOI":"10.1167\/iovs.08-3018"},{"issue":"3","key":"1671_CR3","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1109\/42.845178","volume":"19","author":"AD Hoover","year":"2000","unstructured":"Hoover, A.D., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging. 19(3), 203\u2013210 (2000)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1671_CR4","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr46437.2021.01629","author":"S Shit","year":"2021","unstructured":"Shit, S., Paetzold, J.C., Sekuboyina, A., Ezhov, I., Unger, A., Zhylka, A., Pluim, J.P.W., Bauer, U., Menze, B.H.: clDice - a Novel Topology-preserving loss function for tubular structure segmentation. 2021 IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR). (2021). https:\/\/doi.org\/10.1109\/cvpr46437.2021.01629","journal-title":"2021 IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR)"},{"key":"1671_CR5","doi-asserted-by":"crossref","unstructured":"Olaf Ronneberger, P., Fischer, T., Brox: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 234\u2013241. Springer International Publishing (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1671_CR6","doi-asserted-by":"crossref","unstructured":"Qi, Y., He, Y., Qi, X., Zhang, Y., Yang, G.: Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6070\u20136079 (2023)","DOI":"10.1109\/ICCV51070.2023.00558"},{"key":"1671_CR7","doi-asserted-by":"crossref","unstructured":"Yang, Z., Farsiu, S.: Directional connectivity-based segmentation of medical images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11525\u201311535 (2023)","DOI":"10.1109\/CVPR52729.2023.01109"},{"key":"1671_CR8","doi-asserted-by":"publisher","unstructured":"Wu, H., Wang, W., Zhong, J., Lei, B., Wen, Z., Qin, J.: SCS-Net: A scale and context sensitive network for retinal vessel segmentation. Med. Image. Anal. 70 (2021). https:\/\/doi.org\/10.1016\/j.media.2021.102025","DOI":"10.1016\/j.media.2021.102025"},{"key":"1671_CR9","doi-asserted-by":"crossref","unstructured":"Rahman, M., Marculescu, R.: G-CASCADE: efficient cascaded graph convolutional decoding for 2D medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 7728\u20137737 (2024)","DOI":"10.1109\/WACV57701.2024.00755"},{"key":"1671_CR10","first-page":"3","volume-title":"UNet: A Nested U-Net Architecture for Medical Image Segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: UNet: A Nested U-Net Architecture for Medical Image Segmentation. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, pp. 3\u201311. Springer, Cham (2018)"},{"key":"1671_CR11","unstructured":"Ozan Oktay, J., Schlemper, L.L., Folgoc, M., Lee, M., Heinrich, K., Misawa, K., Mori, S., McDonagh, Nils, Y., Hammerla: Bernhard Kainz attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"1671_CR12","unstructured":"Loshchilov, I.: Decoupled weight decay regularization. arXiv Preprint arXiv:1711.05101 (2017)"},{"key":"1671_CR13","doi-asserted-by":"publisher","unstructured":"Liu, W., Yang, H., Tian, T., Cao, Z., Pan, X., Xu, W., Jin, Y., Gao, F.: Full-resolution network and dual-threshold iteration for retinal vessel and coronary angiograph segmentation. IEEE J. Biomedical Health Inf., 4623\u20134634 (2022). https:\/\/doi.org\/10.1109\/jbhi.2022.3188710","DOI":"10.1109\/jbhi.2022.3188710"},{"key":"1671_CR14","doi-asserted-by":"publisher","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2018). https:\/\/doi.org\/10.1109\/cvpr.2018.00745","DOI":"10.1109\/cvpr.2018.00745"},{"key":"1671_CR15","unstructured":"Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces. arXiv Preprint arXiv:2312.00752 (2023)"},{"key":"1671_CR16","unstructured":"Ma, J., Li, F., Wang, B.: U-mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv preprint arXiv:2401.04722 (2024)"},{"key":"1671_CR17","doi-asserted-by":"crossref","unstructured":"Xing, Z., et al.: Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation. arXiv Preprint arXiv:2401.13560 (2024)","DOI":"10.1007\/978-3-031-72111-3_54"},{"key":"1671_CR18","unstructured":"Dao, T., Gu, A.: Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality. arXiv Preprint arXiv:2405.21060 (2024)"},{"key":"1671_CR19","first-page":"14541","volume":"35","author":"Z Pan","year":"2022","unstructured":"Pan, Z., Cai, J., Zhuang, B.: Fast vision transformers with hilo attention. Adv. Neural. Inf. Process. Syst. 35, 14541\u201314554 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"5","key":"1671_CR20","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1007\/s00371-022-02456-8","volume":"39","author":"D Li","year":"2023","unstructured":"Li, D., et al.: Retinal vessel segmentation by using AFNet. Visual Comput. 39(5), 1929\u20131941 (2023)","journal-title":"Visual Comput."},{"issue":"3","key":"1671_CR21","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1111\/srt.12977","volume":"27","author":"U Sabina","year":"2021","unstructured":"Sabina, U., Whangbo, T.K.: Edge-based effective active appearance model for real\u2010time wrinkle detection. Skin. Res. Technol. 27(3), 444\u2013452 (2021)","journal-title":"Skin. Res. Technol."},{"key":"1671_CR22","doi-asserted-by":"crossref","unstructured":"Dai, J., et al.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"1671_CR23","unstructured":"Zhao, Y., et al.: A battle of network structures: an empirical study of cnn, transformer, and mlp. arXiv preprint arXiv:2108.13002 (2021)"},{"key":"1671_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/iccv48922.2021.00986","author":"Z Liu","year":"2021","unstructured":"Liu, Z., et al.: Swin Transformer: Hierarchical vision transformer using shifted Windows. In: 2021 IEEE\/CVF Int. Conf. Comput. Vis. (ICCV) (2021). https:\/\/doi.org\/10.1109\/iccv48922.2021.00986","journal-title":"2021 IEEE\/CVF Int. Conf. Comput. Vis. (ICCV)"},{"key":"1671_CR25","first-page":"1","volume-title":"2MGAS-Net: multi-level multi-scale Gated Attentional Squeezed Network for Polyp Segmentation","author":"I Bakkouri","year":"2024","unstructured":"Bakkouri, I., Bakkouri, S.: 2MGAS-Net: multi-level multi-scale Gated Attentional Squeezed Network for Polyp Segmentation, pp. 1\u201310. Signal, Image and Video Processing (2024)"},{"key":"1671_CR26","doi-asserted-by":"crossref","unstructured":"Bakkouri, I., Afdel, K.: DermoNet: A computer-aided diagnosis system for dermoscopic disease recognition. Lecture Notes in Computer Science, pp. 170\u2013177 (2020)","DOI":"10.1007\/978-3-030-51935-3_18"},{"key":"1671_CR27","doi-asserted-by":"crossref","unstructured":"Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics gems IV, pp. 474\u2013485 (1994)","DOI":"10.1016\/B978-0-12-336156-1.50061-6"},{"key":"1671_CR28","doi-asserted-by":"publisher","unstructured":"Ronneberger, O., et al.: U-Net: Convolutional Networks for Biomedical Image Segmentation. Lecture Notes in Computer Science,Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015, pp. 234\u201341 (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1671_CR29","unstructured":"Vaswani, A, et al.: Attention is all you need. In: Neural Information Processing Systems, Neural Information Processing Systems (2017)"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01671-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-025-01671-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-025-01671-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T15:06:08Z","timestamp":1756998368000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-025-01671-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,5]]},"references-count":29,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["1671"],"URL":"https:\/\/doi.org\/10.1007\/s00530-025-01671-2","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5164628\/v1","asserted-by":"object"}]},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,4,5]]},"assertion":[{"value":"27 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We would like to state that this work is original research that has not been published before and is not considered for publication elsewhere.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration of originality"}},{"value":"This article does not contain any studies with human participants performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"182"}}