{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:28:04Z","timestamp":1762432084273,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":14,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811638794"},{"type":"electronic","value":"9789811638800"}],"license":[{"start":{"date-parts":[[2021,8,15]],"date-time":"2021-08-15T00:00:00Z","timestamp":1628985600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,15]],"date-time":"2021-08-15T00:00:00Z","timestamp":1628985600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-16-3880-0_15","type":"book-chapter","created":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T11:02:58Z","timestamp":1628938978000},"page":"134-144","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving Topology Consistency of Retinal Vessel Segmentation via a Double U-Net with Asymmetric Convolution"],"prefix":"10.1007","author":[{"given":"Xiaomin","family":"Li","sequence":"first","affiliation":[]},{"given":"Gengsheng","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,15]]},"reference":[{"key":"15_CR1","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 \u2014 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"},{"key":"15_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"797","DOI":"10.1007\/978-3-030-32239-7_88","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"S Zhang","year":"2019","unstructured":"Zhang, S., et al.: Attention guided network for retinal image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 797\u2013805. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_88"},{"key":"15_CR3","doi-asserted-by":"crossref","unstructured":"Xu, R., Ye, X., Jiang, G., Liu, T., Tanaka, S.: Vessel segmentation via a semantics and multi-scale aggregation network. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1085\u20131089 (2020)","DOI":"10.1109\/ICASSP40776.2020.9052914"},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zou, N., Shen, D., Ji, S.: Non-local U-Nets for biomedical image segmentation. In: AAAI Conference on Artificial Intelligence, pp. 6315\u20136322 (2020)","DOI":"10.1609\/aaai.v34i04.6100"},{"key":"15_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"765","DOI":"10.1007\/978-3-030-59722-1_74","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"S Zhang","year":"2020","unstructured":"Zhang, S., Fu, H., Xu, Y., Liu, Y., Tan, M.: Retinal image segmentation with a structure-texture demixing network. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 765\u2013774. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59722-1_74"},{"key":"15_CR6","unstructured":"Mahapatra, D.: Retinal vasculature segmentation using local saliency maps and generative adversarial networks for image super resolution. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp. 242\u2013250 (2017)"},{"key":"15_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/978-3-030-32239-7_12","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"S Wang","year":"2019","unstructured":"Wang, S., Yu, L., Li, K., Yang, X., Fu, C.-W., Heng, P.-A.: Boundary and entropy-driven adversarial learning for fundus image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 102\u2013110. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_12"},{"key":"15_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1007\/978-3-030-59722-1_59","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"W Chen","year":"2020","unstructured":"Chen, W., et al.: TR-GAN: topology ranking GAN with triplet loss for retinal artery\/vein classification. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 616\u2013625. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59722-1_59"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L.: Densely connected convolutional networks. In: Computer Vision and Pattern Recognition (CVPR), pp. 2261\u20132269 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Ding, X., Guo, Y., Din, G.: ACNet: strengthening the kernel skeletons for powerful CNN via asymmetric convolution blocks. In: International Conference on Computer Vision (ICCV), pp. 1911\u20131920 (2019)","DOI":"10.1109\/ICCV.2019.00200"},{"issue":"4","key":"15_CR11","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.D., Niemeijer, M.: Ridge-based vessel segmentation in color images of the retina. Trans. Med. Imaging (TMI) 23(4), 501\u2013509 (2004)","journal-title":"Trans. Med. Imaging (TMI)"},{"key":"15_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/978-3-030-32239-7_11","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"RJ Ara\u00fajo","year":"2019","unstructured":"Ara\u00fajo, R.J., Cardoso, J.S., Oliveira, H.P.: A deep learning design for improving topology coherence in blood vessel segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 93\u2013101. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_11"},{"key":"15_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1007\/978-3-030-59722-1_76","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"R Xu","year":"2020","unstructured":"Xu, R., Liu, T., Ye, X., Lin, L., Chen, Y.-W.: Boosting connectivity in retinal vessel segmentation via a recursive semantics-guided network. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12265, pp. 786\u2013795. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59722-1_76"},{"key":"15_CR14","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.eswa.2018.06.034","volume":"112","author":"A Oliveira","year":"2018","unstructured":"Oliveira, A., Pereira, S., Silva, C.A.: Retinal vessel segmentation based on fully convolutional neural networks. Expert Syst. Appl. 112, 229\u2013242 (2018)","journal-title":"Expert Syst. Appl."}],"container-title":["Lecture Notes in Electrical Engineering","Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021)"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-3880-0_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,7]],"date-time":"2023-01-07T11:07:12Z","timestamp":1673089632000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-3880-0_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,15]]},"ISBN":["9789811638794","9789811638800"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-3880-0_15","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"type":"print","value":"1876-1100"},{"type":"electronic","value":"1876-1119"}],"subject":[],"published":{"date-parts":[[2021,8,15]]},"assertion":[{"value":"15 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICAD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Imaging and Computer-Aided Diagnosis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Birmingham","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 March 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 March 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"micad2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}