{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T11:10:16Z","timestamp":1771067416802,"version":"3.50.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031164422","type":"print"},{"value":"9783031164439","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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\/978-3-031-16443-9_41","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T09:30:11Z","timestamp":1663234211000},"page":"422-432","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Weakly Supervised Volumetric Image Segmentation with Deformed Templates"],"prefix":"10.1007","author":[{"given":"Udaranga","family":"Wickramasinghe","sequence":"first","affiliation":[]},{"given":"Patrick","family":"Jensen","sequence":"additional","affiliation":[]},{"given":"Mian","family":"Shah","sequence":"additional","affiliation":[]},{"given":"Jiancheng","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Pascal","family":"Fua","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"41_CR1","doi-asserted-by":"crossref","unstructured":"Acuna, D., Kar, A., Fidler, S.: Devil is in the edges: learning semantic boundaries from noisy annotations. In: Conference on Computer Vision and Pattern Recognition (2019)","DOI":"10.1109\/CVPR.2019.01133"},{"key":"41_CR2","doi-asserted-by":"crossref","unstructured":"Akuna, D., Ling, H., Kar, A., Fidler, S.: Efficient interactive annotation of segmentation datasets with Polygon-RNN++. In: Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00096"},{"key":"41_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424\u2013432. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49"},{"key":"41_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1007\/978-3-030-32248-9_40","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"AV Dalca","year":"2019","unstructured":"Dalca, A.V., Yu, E., Golland, P., Fischl, B., Sabuncu, M.R., Eugenio Iglesias, J.: Unsupervised deep learning for Bayesian brain MRI segmentation. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 356\u2013365. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_40"},{"key":"41_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1007\/978-3-030-59710-8_47","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"R Dorent","year":"2020","unstructured":"Dorent, R., et al.: Scribble-based domain adaptation via co-segmentation. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12261, pp. 479\u2013489. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_47"},{"key":"41_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1007\/978-3-319-66179-7_65","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"X Feng","year":"2017","unstructured":"Feng, X., Yang, J., Laine, A.F., Angelini, E.D.: Discriminative localization in CNNs for weakly-supervised segmentation of pulmonary nodules. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 568\u2013576. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_65"},{"key":"41_CR7","doi-asserted-by":"crossref","unstructured":"Freedman, D., Zhang, T.: Interactive graph-cut based segmentation with shape priors. In: Conference on Computer Vision and Pattern Recognition, pp. 755\u201362 (2005)","DOI":"10.1109\/CVPR.2005.191"},{"key":"41_CR8","doi-asserted-by":"crossref","unstructured":"Ge, W., Yanga, S., Yu, Y.: Multi-evidence filtering and fusion for multi-label classification, object detection and semantic segmentation based on weakly supervised learning. In: Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00139"},{"key":"41_CR9","unstructured":"Hsu, C., Hsu, K., Tsai, C., Lin, Y., Chuang, Y.: Weakly supervised instance segmentation using the bounding box tightness prior. In: Advances in Neural Information Processing Systems (2019)"},{"key":"41_CR10","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, X., Wang, J., Liu, W., Wang, J.: Weakly-supervised semantic segmentation network with deep seeded region growing. In: Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00733"},{"key":"41_CR11","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1038\/s41592-018-0049-4","volume":"15","author":"M Januszewski","year":"2018","unstructured":"Januszewski, M., Jain, V.: High-precision automated reconstruction of neurons with flood-filling networks. Nat. Methods 15, 605\u2013610 (2018)","journal-title":"Nat. Methods"},{"issue":"4","key":"41_CR12","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/BF00133570","volume":"1","author":"M Kass","year":"1988","unstructured":"Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321\u2013331 (1988)","journal-title":"Int. J. Comput. Vis."},{"key":"41_CR13","doi-asserted-by":"crossref","unstructured":"Kavur, A., Selver, M.: CHAOS challenge - combined (CT-MR) healthy abdominal organ segmentation. arXiv Preprint (2020)","DOI":"10.1016\/j.media.2020.101950"},{"key":"41_CR14","doi-asserted-by":"crossref","unstructured":"Khoreva, A., Benenson, R., Hosang, J., Hein, M., Schiele, B.: Simple does it: weakly supervised instance and semantic segmentation. In: Conference on Computer Vision and Pattern Recognition, pp. 1665\u20131674 (2017)","DOI":"10.1109\/CVPR.2017.181"},{"key":"41_CR15","doi-asserted-by":"crossref","unstructured":"Ling, H., Gao, J., Kar, A., Chen, W., Fidler, S.: Fast interactive object annotation with curve-GCN. In: Conference on Computer Vision and Pattern Recognition, pp. 5257\u20135266 (2019)","DOI":"10.1109\/CVPR.2019.00540"},{"key":"41_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/978-3-030-87196-3_29","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"X Liu","year":"2021","unstructured":"Liu, X., Thermos, S., O\u2019Neil, A., Tsaftaris, S.A.: Semi-supervised meta-learning with disentanglement for domain-generalised medical image segmentation. In: de Bruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (eds.) MICCAI 2021. LNCS, vol. 12902, pp. 307\u2013317. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87196-3_29"},{"key":"41_CR17","doi-asserted-by":"crossref","unstructured":"Maninis, K., Caelles, S., Pont-Tuset, J., Gool, L.: Deep extreme cut: from extreme points to object segmentation. In: Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00071"},{"key":"41_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1007\/978-3-030-00937-3_84","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"Z Mirikharaji","year":"2018","unstructured":"Mirikharaji, Z., Hamarneh, G.: Star shape prior in fully convolutional networks for skin lesion segmentation. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 737\u2013745. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00937-3_84"},{"key":"41_CR19","doi-asserted-by":"crossref","unstructured":"Mortensen, E., Barrett, W.: Intelligent scissors for image composition. In: ACM SIGGRAPH, pp. 191\u2013198, August 1995","DOI":"10.1145\/218380.218442"},{"key":"41_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1007\/978-3-319-46478-7_34","volume-title":"Computer Vision \u2013 ECCV 2016","author":"A Bearman","year":"2016","unstructured":"Bearman, A., Russakovsky, O., Ferrari, V., Fei-Fei, L.: What\u2019s the point: semantic segmentation with point supervision. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 549\u2013565. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46478-7_34"},{"key":"41_CR21","doi-asserted-by":"crossref","unstructured":"Peng, S., Jiang, W., Pi, H., Li, X., Bao, H., Zhou, X.: Deep snake for real-time instance segmentation. In: Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00856"},{"key":"41_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1007\/978-3-030-33642-4_5","volume-title":"Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention","author":"H Roth","year":"2019","unstructured":"Roth, H., et al.: Weakly supervised segmentation from extreme points. In: Zhou, L., et al. (eds.) LABELS\/HAL-MICCAI\/CuRIOUS 2019. LNCS, vol. 11851, pp. 42\u201350. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33642-4_5"},{"key":"41_CR23","doi-asserted-by":"crossref","unstructured":"Shvets, A., Iglovikov, V.: Automatic instrument segmentation in robot-assisted surgery using deep learning. arXiv Preprint (2018)","DOI":"10.1101\/275867"},{"key":"41_CR24","unstructured":"Simpson, A., Menze, B.: A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv Preprint (2019)"},{"key":"41_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/978-3-030-00931-1_76","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"H Spitzer","year":"2018","unstructured":"Spitzer, H., Kiwitz, K., Amunts, K., Harmeling, S., Dickscheid, T.: Improving cytoarchitectonic segmentation of human brain areas with self-supervised Siamese networks. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 663\u2013671. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_76"},{"key":"41_CR26","doi-asserted-by":"crossref","unstructured":"Wang, Z., Acuna, D., Ling, H., Kar, A., Fidler, S.: Object instance annotation with deep extreme level set evolution. In: European Conference on Computer Vision (2020)","DOI":"10.1109\/CVPR.2019.00768"},{"key":"41_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1007\/978-3-030-32239-7_25","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"U Wickramasinghe","year":"2019","unstructured":"Wickramasinghe, U., Knott, G., Fua, P.: Probabilistic atlases to enforce topological constraints. In: Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 218\u2013226. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_25"},{"key":"41_CR28","doi-asserted-by":"crossref","unstructured":"Wickramasinghe, U., Knott, G., Fua, P.: Deep active surface models. In: Conference on Computer Vision and Pattern Recognition (2021)","DOI":"10.1109\/CVPR46437.2021.01148"},{"key":"41_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1007\/978-3-030-59719-1_30","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"U Wickramasinghe","year":"2020","unstructured":"Wickramasinghe, U., Remelli, E., Knott, G., Fua, P.: Voxel2Mesh: 3D mesh model generation from volumetric data. In: Martel, A.L., Abolmaesumi, P., Stoyanov, D., Mateus, D., Zuluaga, M.A., Zhou, S.K., Racoceanu, D., Joskowicz, L. (eds.) MICCAI 2020. LNCS, vol. 12264, pp. 299\u2013308. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59719-1_30"},{"key":"41_CR30","doi-asserted-by":"crossref","unstructured":"Wolf, I., et al.: The medical imaging interaction toolkit (MITK): a toolkit facilitating the creation of interactive software by extending VTK and ITK. In: Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display (2004)","DOI":"10.1117\/12.535112"},{"key":"41_CR31","unstructured":"Xia, X., Kulis, B.: W-Net: a deep model for fully unsupervised image segmentation. arXiv Preprint (2017)"},{"key":"41_CR32","doi-asserted-by":"crossref","unstructured":"Yang, L., Wang, Y., Xiong, X., Yang, J., Katsaggelos, A.: Efficient video object segmentation via network modulation. In: Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00680"},{"key":"41_CR33","doi-asserted-by":"publisher","first-page":"2736","DOI":"10.1109\/TMI.2020.3046292","volume":"40","author":"T Zhao","year":"2020","unstructured":"Zhao, T., Yin, Z.: Weakly supervised cell segmentation by point annotation. IEEE Trans. Med. Imaging 40, 2736\u20132747 (2020)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16443-9_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T22:32:16Z","timestamp":1727994736000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16443-9_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164422","9783031164439"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16443-9_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"16 September 2022","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":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2022","order":10,"name":"conference_id","label":"Conference ID","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 Conference","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1831","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":"574","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":"31% - 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)"}}]}}