{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T18:25:21Z","timestamp":1772907921539,"version":"3.50.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031439001","type":"print"},{"value":"9783031439018","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43901-8_31","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:08:23Z","timestamp":1696115303000},"page":"323-332","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Elongated Physiological Structure Segmentation via\u00a0Spatial and\u00a0Scale Uncertainty-Aware Network"],"prefix":"10.1007","author":[{"given":"Yinglin","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiling","family":"Xi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huazhu","family":"Fu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dave","family":"Towey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"RuiBin","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Risa","family":"Higashita","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"31_CR1","unstructured":"Chen, J., et al.: Transunet: transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"31_CR2","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of ICML. Proceedings of Machine Learning Research, vol. 48, pp. 1050\u20131059. PMLR, New York, New York, USA (20\u201322 Jun 2016)"},{"key":"31_CR3","unstructured":"Gawlikowski, J., et al.: A survey of uncertainty in deep neural networks. arXiv preprint arXiv:2107.03342 (2021)"},{"key":"31_CR4","doi-asserted-by":"crossref","unstructured":"Guo, Changlu, et al.: Sa-unet: spatial attention u-net for retinal vessel segmentation. In: Proceedings of ICPR, pp. 1236\u20131242 (2021)","DOI":"10.1109\/ICPR48806.2021.9413346"},{"issue":"1","key":"31_CR5","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 (2022)","journal-title":"Sci. Data"},{"key":"31_CR6","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? Proc. NeurIPS 30 (2017)"},{"key":"31_CR7","unstructured":"Kohl, S., et al.: A probabilistic u-net for segmentation of ambiguous images. Proc. of NeurIPS 31 (2018)"},{"key":"31_CR8","unstructured":"Lakshminarayanan, B., et al.: Simple and scalable predictive uncertainty estimation using deep ensembles. Proc. of NeurIPS 30 (2017)"},{"issue":"6","key":"31_CR9","doi-asserted-by":"publisher","first-page":"2406","DOI":"10.3390\/s22062406","volume":"22","author":"J Lee","year":"2022","unstructured":"Lee, J., et al.: Method to minimize the errors of AI: quantifying and exploiting uncertainty of deep learning in brain tumor segmentation. Sensors 22(6), 2406 (2022)","journal-title":"Sensors"},{"issue":"1","key":"31_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-17876-z","volume":"7","author":"C Leibig","year":"2017","unstructured":"Leibig, C., et al.: Leveraging uncertainty information from deep neural networks for disease detection. Sci. Rep. 7(1), 1\u201314 (2017)","journal-title":"Sci. Rep."},{"key":"31_CR11","doi-asserted-by":"crossref","unstructured":"Li, L., et al.: Iternet: Retinal image segmentation utilizing structural redundancy in vessel networks. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3656\u20133665 (2020)","DOI":"10.1109\/WACV45572.2020.9093621"},{"issue":"9","key":"31_CR12","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."},{"issue":"12","key":"31_CR13","doi-asserted-by":"publisher","first-page":"3868","DOI":"10.1109\/TMI.2020.3006437","volume":"39","author":"A Mehrtash","year":"2020","unstructured":"Mehrtash, A., et al.: Confidence calibration and predictive uncertainty estimation for deep medical image segmentation. IEEE Trans. Med. Imaging 39(12), 3868\u20133878 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"31_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101874","volume":"67","author":"L Mou","year":"2021","unstructured":"Mou, L., et al.: Cs2-net: deep learning segmentation of curvilinear structures in medical imaging. Med. Image Anal. 67, 101874 (2021)","journal-title":"Med. Image Anal."},{"key":"31_CR15","unstructured":"Neal, R.M.: Bayesian learning for neural networks. IEEE Trans. Neural Netw. (1994)"},{"key":"31_CR16","unstructured":"Oktay, O., et al.: Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"31_CR17","unstructured":"Pearce, T., et al.: Understanding softmax confidence and uncertainty. arXiv preprint arXiv:2106.04972 (2021)"},{"key":"31_CR18","doi-asserted-by":"crossref","unstructured":"Pidaparthy, H., et al.: Automatic play segmentation of hockey videos. In: Proceedings of CVPR, pp. 4585\u20134593 (2021)","DOI":"10.1109\/CVPRW53098.2021.00516"},{"key":"31_CR19","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":"31_CR20","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1136\/bjo.2009.166561","volume":"94","author":"A Ruggeri","year":"2010","unstructured":"Ruggeri, A., et al.: A system for the automatic estimation of morphometric parameters of corneal endothelium in Alizarine red-stained images. Br. J. Ophthalmol. 94(5), 643\u2013647 (2010)","journal-title":"Br. J. Ophthalmol."},{"issue":"1","key":"31_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-015-0054-3","volume":"15","author":"B Selig","year":"2015","unstructured":"Selig, B., et al.: Fully automatic evaluation of the corneal endothelium from in vivo confocal microscopy. BMC Med. Imaging 15(1), 1\u201315 (2015)","journal-title":"BMC Med. Imaging"},{"issue":"1","key":"31_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-015-0068-x","volume":"15","author":"AA Taha","year":"2015","unstructured":"Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15(1), 1\u201328 (2015)","journal-title":"BMC Med. Imaging"},{"key":"31_CR23","doi-asserted-by":"crossref","unstructured":"Wang, L., et al.: Medical matting: a new perspective on medical segmentation with uncertainty. In: Proceedings of MICCAI, pp. 573\u2013583 (2021)","DOI":"10.1007\/978-3-030-87199-4_54"},{"key":"31_CR24","doi-asserted-by":"crossref","unstructured":"Xie, Y., et al.: Uncertainty-aware cascade network for ultrasound image segmentation with ambiguous boundary. In: Proceedings of MICCAI, pp. 268\u2013278 (2022)","DOI":"10.1007\/978-3-031-16440-8_26"},{"key":"31_CR25","doi-asserted-by":"crossref","unstructured":"Yang, H., et al.: Uncertainty-guided lung nodule segmentation with feature-aware attention. In: Proceedings of MICCAI, pp. 44\u201354 (2022)","DOI":"10.1007\/978-3-031-16443-9_5"},{"key":"31_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: A multi-branch hybrid transformer network for corneal endothelial cell segmentation. In: Proceedings of MICCAI, pp. 99\u2013108 (2021)","DOI":"10.1007\/978-3-030-87193-2_10"},{"issue":"9","key":"31_CR27","doi-asserted-by":"publisher","first-page":"2725","DOI":"10.1109\/TMI.2020.2974499","volume":"39","author":"Y Zhao","year":"2020","unstructured":"Zhao, Y., et al.: Automated tortuosity analysis of nerve fibers in corneal confocal microscopy. IEEE Trans. Med. Imaging 39(9), 2725\u20132737 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"31_CR28","doi-asserted-by":"crossref","unstructured":"Zhou, L., et al.: D-linknet: linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In: Proceedings of CVPR, pp. 182\u2013186 (June 2018)","DOI":"10.1109\/CVPRW.2018.00034"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43901-8_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T15:21:44Z","timestamp":1710170504000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43901-8_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439001","9783031439018"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43901-8_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"730","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}