{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T20:59:52Z","timestamp":1768424392740,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":40,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819549863","type":"print"},{"value":"9789819549870","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-95-4987-0_12","type":"book-chapter","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T12:29:34Z","timestamp":1768393774000},"page":"159-172","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unifying Vision and\u00a0Language in\u00a0SAM for\u00a0Robust Curvilinear Structure Segmentation"],"prefix":"10.1007","author":[{"given":"Dianshuo","family":"Li","sequence":"first","affiliation":[]},{"given":"Li","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Li","family":"Shuo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,15]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Al-Huda, Z., Peng, B., Al-antari, M.A., Algburi, R.N.A., Saleh, R.A., Moghalles, K.: Mdau-net: a multi-scale u-net with dual attention module for pavement crack segmentation. In: 2023 18th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), pp. 170\u2013177. IEEE (2023)","DOI":"10.1109\/ISKE60036.2023.10481232"},{"key":"12_CR2","doi-asserted-by":"crossref","unstructured":"Alom, M.Z., Yakopcic, C., Taha, T.M., Asari, V.K.: Nuclei segmentation with recurrent residual convolutional neural networks based u-net (r2u-net). In: NAECON 2018-IEEE National Aerospace and Electronics Conference, pp. 228\u2013233. IEEE (2018)","DOI":"10.1109\/NAECON.2018.8556686"},{"issue":"4","key":"12_CR3","first-page":"2359","volume":"169","author":"J B\u00fchler","year":"2015","unstructured":"B\u00fchler, J., et al.: Phenovein\u2013a tool for leaf vein segmentation and analysis. Plant Physiol. 169(4), 2359\u20132370 (2015)","journal-title":"Plant Physiol."},{"key":"12_CR4","unstructured":"Chen, Z., et al.: Vision transformer adapter for dense predictions. arXiv preprint arXiv:2205.08534 (2022)"},{"key":"12_CR5","doi-asserted-by":"publisher","unstructured":"Cherukuri, V., Kumar\u00a0B.G., V., Bala, R., Monga, V.: Deep retinal image segmentation with regularization under geometric priors. IEEE Trans. Image Process. 29, 2552\u20132567 (2020). https:\/\/doi.org\/10.1109\/TIP.2019.2946078","DOI":"10.1109\/TIP.2019.2946078"},{"issue":"3","key":"12_CR6","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1109\/42.845178","volume":"19","author":"A Hoover","year":"2000","unstructured":"Hoover, A., 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). https:\/\/doi.org\/10.1109\/42.845178","journal-title":"IEEE Trans. Med. Imaging"},{"key":"12_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106732","volume":"218","author":"S Hussain","year":"2022","unstructured":"Hussain, S., Guo, F., Li, W., Shen, Z.: Dilunet: a u-net based architecture for blood vessels segmentation. Comput. Methods Programs Biomed. 218, 106732 (2022). https:\/\/doi.org\/10.1016\/j.cmpb.2022.106732","journal-title":"Comput. Methods Programs Biomed."},{"issue":"1","key":"12_CR8","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1038\/s41597-022-01564-3","volume":"9","author":"K Jin","year":"2022","unstructured":"Jin, K., et al.: Fives: a fundus image dataset for artificial intelligence based vessel segmentation. Sci. Data 9(1), 475\u2013483 (2022)","journal-title":"Sci. Data"},{"key":"12_CR9","unstructured":"Ke, L., et al.: Segment anything in high quality. In: Advances in Neural Information Processing Systems, vol.\u00a036, pp. 29914\u201329934 (2023)"},{"key":"12_CR10","doi-asserted-by":"publisher","unstructured":"Kirillov, A., et al.: Segment anything. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3992\u20134003 (2023). https:\/\/doi.org\/10.1109\/ICCV51070.2023.00371","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Kondo, Y., Ukita, N.: Crack segmentation for low-resolution images using joint learning with super-resolution. In: 2021 17th International Conference on Machine Vision and Applications (MVA), pp.\u00a01\u20136. IEEE (2021)","DOI":"10.23919\/MVA51890.2021.9511400"},{"key":"12_CR12","doi-asserted-by":"publisher","unstructured":"Li, F., et al.: Mask DINO: towards a unified transformer-based framework for object detection and segmentation. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3041\u20133050 (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.00297","DOI":"10.1109\/CVPR52729.2023.00297"},{"key":"12_CR13","doi-asserted-by":"publisher","first-page":"103092","DOI":"10.1016\/j.media.2024.103092","volume":"93","author":"M Li","year":"2024","unstructured":"Li, M., et al.: Octa-500: a retinal dataset for optical coherence tomography angiography study. Med. Image Anal. 93, 103092\u2013103108 (2024). https:\/\/doi.org\/10.1016\/j.media.2024.103092","journal-title":"Med. Image Anal."},{"key":"12_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1007\/978-3-030-87589-3_40","volume-title":"Machine Learning in Medical Imaging","author":"Y Li","year":"2021","unstructured":"Li, Y., et al.: GT U-Net: a U-net like group transformer network for tooth root segmentation. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds.) MLMI 2021. LNCS, vol. 12966, pp. 386\u2013395. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87589-3_40"},{"key":"12_CR15","doi-asserted-by":"publisher","unstructured":"Liu, H., Miao, X., Mertz, C., Xu, C., Kong, H.: Crackformer: transformer network for fine-grained crack detection. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 3763\u20133772 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00376","DOI":"10.1109\/ICCV48922.2021.00376"},{"issue":"9","key":"12_CR16","doi-asserted-by":"publisher","first-page":"4623","DOI":"10.1109\/JBHI.2022.3188710","volume":"26","author":"W Liu","year":"2022","unstructured":"Liu, W., et al.: Full-resolution network and dual-threshold iteration for retinal vessel and coronary angiograph segmentation. IEEE J. Biomed. Health Inform. 26(9), 4623\u20134634 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"issue":"3","key":"12_CR18","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1016\/S1361-8415(01)00040-8","volume":"5","author":"LM Lorigo","year":"2001","unstructured":"Lorigo, L.M., et al.: Curves: curve evolution for vessel segmentation. Med. Image Anal. 5(3), 195\u2013206 (2001)","journal-title":"Med. Image Anal."},{"key":"12_CR19","doi-asserted-by":"publisher","unstructured":"Ma, Y., et al.: Self-supervised vessel segmentation via adversarial learning. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 7516\u20137525 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00744","DOI":"10.1109\/ICCV48922.2021.00744"},{"key":"12_CR20","unstructured":"Middha, L.: Kaggle crack segmentation dataset (2020). https:\/\/www.kaggle.com\/datasets\/lakshaymiddha\/crack-segmentation-dataset. Accessed 2020"},{"key":"12_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/978-3-030-32239-7_80","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"L Mou","year":"2019","unstructured":"Mou, L., et al.: CS-Net: channel and spatial attention network for curvilinear structure segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 721\u2013730. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_80"},{"key":"12_CR22","doi-asserted-by":"publisher","unstructured":"Ning, H., Wang, C., Chen, X., Li, S.: An accurate and efficient neural network for octa vessel segmentation and a new dataset. In: ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 1966\u20131970 (2024). https:\/\/doi.org\/10.1109\/ICASSP48485.2024.10447708","DOI":"10.1109\/ICASSP48485.2024.10447708"},{"key":"12_CR23","unstructured":"Open, A.: Gpt-4v (ision) system card 2023 (2023). https:\/\/cdnopenai.com\/papers\/GPTV_System_Card.pdf"},{"issue":"5","key":"12_CR24","doi-asserted-by":"publisher","first-page":"2004","DOI":"10.1167\/iovs.08-3018","volume":"50","author":"CG Owen","year":"2009","unstructured":"Owen, C.G., et al.: Measuring retinal vessel tortuosity in 10-year-old children: validation of the computer-assisted image analysis of the retina (caiar) program. Invest. Ophthalmol. Visual Sci. 50(5), 2004\u20132010 (2009)","journal-title":"Invest. Ophthalmol. Visual Sci."},{"key":"12_CR25","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision (2021). https:\/\/arxiv.org\/abs\/2103.00020"},{"key":"12_CR26","doi-asserted-by":"publisher","unstructured":"Rao, Y., et al.: Denseclip: language-guided dense prediction with context-aware prompting. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18061\u201318070 (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.01755","DOI":"10.1109\/CVPR52688.2022.01755"},{"key":"12_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"issue":"5","key":"12_CR28","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1049\/ip-vis:19952140","volume":"142","author":"PL Rosin","year":"1995","unstructured":"Rosin, P.L., West, G.A.: Curve segmentation and representation by superellipses. IEE Proc.-Vis. Image Signal Process. 142(5), 280\u2013288 (1995)","journal-title":"IEE Proc.-Vis. Image Signal Process."},{"key":"12_CR29","doi-asserted-by":"publisher","unstructured":"Shen, Y., et al.: Aligning and prompting everything all at once for universal visual perception. In: 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13193\u201313203 (2024). https:\/\/doi.org\/10.1109\/CVPR52733.2024.01253","DOI":"10.1109\/CVPR52733.2024.01253"},{"issue":"12","key":"12_CR30","doi-asserted-by":"publisher","first-page":"3434","DOI":"10.1109\/TITS.2016.2552248","volume":"17","author":"Y Shi","year":"2016","unstructured":"Shi, Y., Cui, L., Qi, Z., Meng, F., Chen, Z.: Automatic road crack detection using random structured forests. IEEE Trans. Intell. Transp. Syst. 17(12), 3434\u20133445 (2016)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"4","key":"12_CR31","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"J Staal","year":"2004","unstructured":"Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501\u2013509 (2004). https:\/\/doi.org\/10.1109\/TMI.2004.825627","journal-title":"IEEE Trans. Med. Imaging"},{"key":"12_CR32","doi-asserted-by":"crossref","unstructured":"Tao, H., Liu, B., Cui, J., Zhang, H.: A convolutional-transformer network for crack segmentation with boundary awareness. In: 2023 IEEE International Conference on Image Processing (ICIP), pp. 86\u201390. IEEE (2023)","DOI":"10.1109\/ICIP49359.2023.10222276"},{"issue":"1","key":"12_CR33","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1175\/1520-0477(1998)079<0061:APGTWA>2.0.CO;2","volume":"79","author":"C Torrence","year":"1998","unstructured":"Torrence, C., Compo, G.P.: A practical guide to wavelet analysis. Bull. Am. Meteor. Soc. 79(1), 61\u201378 (1998)","journal-title":"Bull. Am. Meteor. Soc."},{"issue":"2","key":"12_CR34","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1109\/TMI.2006.889722","volume":"26","author":"JA Tyrrell","year":"2007","unstructured":"Tyrrell, J.A., et al.: Robust 3-D modeling of vasculature imagery using superellipsoids. IEEE Trans. Med. Imaging 26(2), 223\u2013237 (2007). https:\/\/doi.org\/10.1109\/TMI.2006.889722","journal-title":"IEEE Trans. Med. Imaging"},{"key":"12_CR35","doi-asserted-by":"publisher","unstructured":"Wei, Z., et al.: Stronger, fewer, & superior: harnessing vision foundation models for domain generalized semantic segmentation. In: 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 28619\u201328630 (2024). https:\/\/doi.org\/10.1109\/CVPR52733.2024.02704","DOI":"10.1109\/CVPR52733.2024.02704"},{"key":"12_CR36","doi-asserted-by":"publisher","unstructured":"Xu, J., et al.: Groupvit: semantic segmentation emerges from text supervision. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 18113\u201318123 (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.01760","DOI":"10.1109\/CVPR52688.2022.01760"},{"key":"12_CR37","doi-asserted-by":"publisher","first-page":"106960","DOI":"10.1016\/j.compbiomed.2023.106960","volume":"159","author":"H Zhang","year":"2023","unstructured":"Zhang, H., et al.: BCU-Net: bridging convnext and u-net for medical image segmentation. Comput. Biol. Med. 159, 106960\u2013106976 (2023)","journal-title":"Comput. Biol. Med."},{"key":"12_CR38","doi-asserted-by":"publisher","first-page":"116526","DOI":"10.1016\/j.eswa.2022.116526","volume":"195","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y., He, M., Chen, Z., Hu, K., Li, X., Gao, X.: Bridge-net: context-involved u-net with patch-based loss weight mapping for retinal blood vessel segmentation. Expert Syst. Appl. 195, 116526\u2013116541 (2022)","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"12_CR39","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","volume":"39","author":"Z Zhou","year":"2020","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans. Med. Imaging 39(6), 1856\u20131867 (2020). https:\/\/doi.org\/10.1109\/TMI.2019.2959609","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"12_CR40","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.patrec.2011.11.004","volume":"33","author":"Q Zou","year":"2012","unstructured":"Zou, Q., Cao, Y., Li, Q., Mao, Q., Wang, S.: Cracktree: automatic crack detection from pavement images. Pattern Recogn. Lett. 33(3), 227\u2013238 (2012)","journal-title":"Pattern Recogn. Lett."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-4987-0_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T12:29:38Z","timestamp":1768393778000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-4987-0_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819549863","9789819549870"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-4987-0_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"15 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}