{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T06:13:34Z","timestamp":1760249614151,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030871987"},{"type":"electronic","value":"9783030871994"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-87199-4_54","type":"book-chapter","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:19:41Z","timestamp":1632377981000},"page":"573-583","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Medical Matting: A New Perspective on\u00a0Medical Segmentation with Uncertainty"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2374-0725","authenticated-orcid":false,"given":"Lin","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6687-7054","authenticated-orcid":false,"given":"Lie","family":"Ju","sequence":"additional","affiliation":[]},{"given":"Donghao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Wanji","family":"He","sequence":"additional","affiliation":[]},{"given":"Yelin","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Zhiwen","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xuan","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Xiufen","family":"Ye","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5880-8673","authenticated-orcid":false,"given":"Zongyuan","family":"Ge","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,21]]},"reference":[{"key":"54_CR1","doi-asserted-by":"crossref","unstructured":"Aksoy, Y., Ozan Aydin, T., Pollefeys, M.: Designing effective inter-pixel information flow for natural image matting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 29\u201337 (2017)","DOI":"10.1109\/CVPR.2017.32"},{"key":"54_CR2","doi-asserted-by":"crossref","unstructured":"Armato III, S.G., et al.: Lung image database consortium: developing a resource for the medical imaging research community. Radiology 232(3), 739\u2013748 (2004)","DOI":"10.1148\/radiol.2323032035"},{"key":"54_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/978-3-030-32245-8_14","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"CF Baumgartner","year":"2019","unstructured":"Baumgartner, C.F., et al.: PHiSeg: capturing uncertainty in medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 119\u2013127. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_14"},{"key":"54_CR4","unstructured":"Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)"},{"key":"54_CR5","doi-asserted-by":"crossref","unstructured":"Cai, S., et al.: Disentangled image matting. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 8819\u20138828 (2019)","DOI":"10.1109\/ICCV.2019.00891"},{"issue":"9","key":"54_CR6","doi-asserted-by":"publisher","first-page":"2175","DOI":"10.1109\/TPAMI.2013.18","volume":"35","author":"Q Chen","year":"2013","unstructured":"Chen, Q., Li, D., Tang, C.K.: KNN Matting. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 35(9), 2175\u20132188 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"key":"54_CR7","doi-asserted-by":"crossref","unstructured":"Cheng, J., Zhao, M., Lin, M., Chiu, B.: AWM: adaptive weight matting for medical image segmentation. In: Medical Imaging 2017: Image Processing, vol. 10133, p. 101332P. International Society for Optics and Photonics (2017)","DOI":"10.1117\/12.2254774"},{"key":"54_CR8","unstructured":"Chuang, Y.Y., Curless, B., Salesin, D.H., Szeliski, R.: A Bayesian approach to digital matting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, p. II. IEEE (2001"},{"issue":"2","key":"54_CR9","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1016\/j.strusafe.2008.06.020","volume":"31","author":"A Der Kiureghian","year":"2009","unstructured":"Der Kiureghian, A., Ditlevsen, O.: Aleatory or epistemic? Does it matter? Struct. Saf. 31(2), 105\u2013112 (2009)","journal-title":"Struct. Saf."},{"issue":"5","key":"54_CR10","doi-asserted-by":"publisher","first-page":"2367","DOI":"10.1109\/TIP.2018.2885495","volume":"28","author":"Z Fan","year":"2018","unstructured":"Fan, Z., Lu, J., Wei, C., Huang, H., Cai, X., Chen, X.: A hierarchical image matting model for blood vessel segmentation in fundus images. IEEE Trans. Image Process. (TIP) 28(5), 2367\u20132377 (2018)","journal-title":"IEEE Trans. Image Process. (TIP)"},{"key":"54_CR11","unstructured":"Forte, M., Piti\u00e9, F.: F, B, Alpha matting. arXiv preprint arXiv:2003.07711 (2020)"},{"key":"54_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-030-60365-6_2","volume-title":"Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis","author":"M Gantenbein","year":"2020","unstructured":"Gantenbein, M., Erdil, E., Konukoglu, E.: RevPHiSeg: a memory-efficient neural network for uncertainty quantification in medical image segmentation. In: Sudre, C.H., et al. (eds.) UNSURE\/GRAIL -2020. LNCS, vol. 12443, pp. 13\u201322. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60365-6_2"},{"key":"54_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: Proceedings of the IEEE International Conference on Computer Vision (CVPR), pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"54_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"54_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-3-030-32245-8_16","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"S Hu","year":"2019","unstructured":"Hu, S., Worrall, D., Knegt, S., Veeling, B., Huisman, H., Welling, M.: Supervised uncertainty quantification for segmentation with multiple annotations. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 137\u2013145. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_16"},{"key":"54_CR16","unstructured":"H\u00fcllermeier, E., Waegeman, W.: Aleatoric and epistemic uncertainty in machine learning: a tutorial introduction. arXiv preprint arXiv:1910.09457 (2019)"},{"key":"54_CR17","doi-asserted-by":"crossref","unstructured":"Kendall, A., Badrinarayanan, V., Cipolla, R.: Bayesian segNet: model uncertainty in deep convolutional encoder-decoder architectures for scene understanding. In: British Machine Vision Conference (BMVC) (2017)","DOI":"10.5244\/C.31.57"},{"key":"54_CR18","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? In: Advances in Neural Information Processing Systems (NIPS), pp. 5574\u20135584 (2017)"},{"key":"54_CR19","unstructured":"Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7482\u20137491 (2018)"},{"key":"54_CR20","unstructured":"Kohl, S., et al.: A probabilistic U-Net for segmentation of ambiguous images. In: Advances in Neural Information Processing Systems (NIPS), pp. 6965\u20136975 (2018)"},{"issue":"2","key":"54_CR21","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1109\/TPAMI.2007.1177","volume":"30","author":"A Levin","year":"2007","unstructured":"Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 30(2), 228\u2013242 (2007)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"key":"54_CR22","doi-asserted-by":"crossref","unstructured":"Li, Y., Lu, H.: Natural image matting via guided contextual attention. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), vol. 34, pp. 11450\u201311457 (2020)","DOI":"10.1609\/aaai.v34i07.6809"},{"key":"54_CR23","unstructured":"Li, Y., Xu, Q., Lu, H.: Hierarchical opacity propagation for image matting. arXiv preprint arXiv:2004.03249 (2020)"},{"key":"54_CR24","unstructured":"Loshchilov, I., Hutter, F.: SGDR: Stochastic gradient descent with warm restarts. Learning 10, 3"},{"key":"54_CR25","unstructured":"Lutz, S., Amplianitis, K., Smolic, A.: AlphaGAN: generative adversarial networks for natural image matting. arXiv preprint arXiv:1807.10088 (2018)"},{"key":"54_CR26","unstructured":"Menze, B., Joskowicz, L., Bakas, S., Jakab, A., Konukoglu, E., Becker, A.: Quantification of uncertainties in biomedical image quantification challenge. [EB\/OL]. https:\/\/qubiq.grand-challenge.org\/Home\/. Accessed 22 Oct 2020"},{"key":"54_CR27","unstructured":"Monteiro, M., et al.: Stochastic segmentation networks: modelling spatially correlated aleatoric uncertainty. arXiv preprint arXiv:2006.06015 (2020)"},{"key":"54_CR28","doi-asserted-by":"crossref","unstructured":"Rupprecht, C., et al.: Learning in an uncertain world: Representing ambiguity through multiple hypotheses. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 3591\u20133600 (2017)","DOI":"10.1109\/ICCV.2017.388"},{"key":"54_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1007\/978-3-319-46448-0_6","volume-title":"Computer Vision \u2013 ECCV 2016","author":"X Shen","year":"2016","unstructured":"Shen, X., Tao, X., Gao, H., Zhou, C., Jia, J.: Deep automatic portrait matting. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 92\u2013107. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_6"},{"key":"54_CR30","unstructured":"Simard, P.Y., Steinkraus, D., Platt, J.C., et al.: Best practices for convolutional neural networks applied to visual document analysis. In: Icdar, vol. 3 (2003)"},{"key":"54_CR31","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2019.01.103","volume":"338","author":"G Wang","year":"2019","unstructured":"Wang, G., Li, W., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T.: Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing 338, 34\u201345 (2019)","journal-title":"Neurocomputing"},{"key":"54_CR32","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., So Kweon, I.: CBAM: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"54_CR33","doi-asserted-by":"crossref","unstructured":"Xu, N., Price, B., Cohen, S., Huang, T.: Deep image matting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2970\u20132979 (2017)","DOI":"10.1109\/CVPR.2017.41"},{"key":"54_CR34","doi-asserted-by":"crossref","unstructured":"Zeng, Z., Wang, J., Shepherd, T., Zwiggelaar, R.: Region-based active surface modelling and alpha matting for unsupervised tumour segmentation in pet. In: IEEE International Conference on Image Processing (ICIP), pp. 1997\u20132000. IEEE (2012)","DOI":"10.1109\/ICIP.2012.6467280"},{"key":"54_CR35","doi-asserted-by":"publisher","first-page":"107068","DOI":"10.1016\/j.patcog.2019.107068","volume":"98","author":"H Zhao","year":"2020","unstructured":"Zhao, H., Li, H., Cheng, L.: Improving retinal vessel segmentation with joint local loss by matting. Pattern Recogn. (PR) 98, 107068 (2020)","journal-title":"Pattern Recogn. (PR)"},{"key":"54_CR36","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Kambhamettu, C.: Learning based digital matting. In: 2009 IEEE 12th International Conference on Computer Vision (ICCV), pp. 889\u2013896. IEEE (2009)","DOI":"10.1109\/ICCV.2009.5459326"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87199-4_54","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T18:48:13Z","timestamp":1725821293000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87199-4_54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030871987","9783030871994"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87199-4_54","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","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":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai2021.org\/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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1622","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":"531","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":"33% - 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":"4","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)"}},{"value":"The conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}