{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T15:10:33Z","timestamp":1777734633219,"version":"3.51.4"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030597092","type":"print"},{"value":"9783030597108","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-59710-8_47","type":"book-chapter","created":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T18:06:41Z","timestamp":1601575601000},"page":"479-489","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Scribble-Based Domain Adaptation via Co-segmentation"],"prefix":"10.1007","author":[{"given":"Reuben","family":"Dorent","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"Joutard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan","family":"Shapey","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sotirios","family":"Bisdas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neil","family":"Kitchen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robert","family":"Bradford","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shakeel","family":"Saeed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marc","family":"Modat","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S\u00e9bastien","family":"Ourselin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tom","family":"Vercauteren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"47_CR1","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1111\/j.1467-8659.2009.01645.x","volume":"29","author":"A Adams","year":"2010","unstructured":"Adams, A., Baek, J., Davis, M.A.: Fast high-dimensional filtering using the permutohedral lattice. Comput. Graph. Forum 29, 753\u2013762 (2010)","journal-title":"Comput. Graph. Forum"},{"key":"47_CR2","doi-asserted-by":"crossref","unstructured":"Baque, P., Bagautdinov, T., Fleuret, F., Fua, P.: Principled parallel mean-field inference for discrete random fields. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016","DOI":"10.1109\/CVPR.2016.630"},{"issue":"2","key":"47_CR3","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/s11263-006-7934-5","volume":"70","author":"Y Boykov","year":"2006","unstructured":"Boykov, Y., Funka-Lea, G.: Graph cuts and efficient ND image segmentation. Int. J. Comput. Vis. 70(2), 109\u2013131 (2006). https:\/\/doi.org\/10.1007\/s11263-006-7934-5","journal-title":"Int. J. Comput. Vis."},{"key":"47_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1007\/978-3-030-00889-5_27","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"YB Can","year":"2018","unstructured":"Can, Y.B., et al.: Learning to segment medical images with scribble-supervision alone. In: Stoyanov, D., et al. (eds.) DLMIA\/ML-CDS -2018. LNCS, vol. 11045, pp. 236\u2013244. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00889-5_27"},{"issue":"1","key":"47_CR5","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1002\/lary.26589","volume":"128","author":"DH Coelho","year":"2018","unstructured":"Coelho, D.H., Tang, Y., Suddarth, B., Mamdani, M.: MRI surveillance of vestibular schwannomas without contrast enhancement: clinical and economic evaluation. Laryngoscope 128(1), 202\u2013209 (2018). https:\/\/doi.org\/10.1002\/lary.26589","journal-title":"Laryngoscope"},{"key":"47_CR6","doi-asserted-by":"crossref","unstructured":"Dou, Q., et al.: Pnp-adanet: plug-and-play adversarial domain adaptation network with a benchmark at cross-modality cardiac segmentation. ArXiv (2018)","DOI":"10.1109\/ACCESS.2019.2929258"},{"key":"47_CR7","series-title":"Advances in Computer Vision and Pattern Recognition","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/978-3-319-58347-1_10","volume-title":"Domain Adaptation in Computer Vision Applications","author":"Y Ganin","year":"2017","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. In: Csurka, G. (ed.) Domain Adaptation in Computer Vision Applications. ACVPR, pp. 189\u2013209. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-58347-1_10"},{"key":"47_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1007\/978-3-319-66179-7_59","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013MICCAI 2017","author":"M Ghafoorian","year":"2017","unstructured":"Ghafoorian, M., et al.: Transfer learning for domain adaptation in MRI: application in brain lesion segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 516\u2013524. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66179-7_59"},{"key":"47_CR9","doi-asserted-by":"crossref","unstructured":"Hochbaum, D.S., Singh, V.: An efficient algorithm for co-segmentation. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 269\u2013276, September 2009","DOI":"10.1109\/ICCV.2009.5459261"},{"key":"47_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/978-3-030-32248-9_20","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"Z Ji","year":"2019","unstructured":"Ji, Z., Shen, Y., Ma, C., Gao, M.: Scribble-based hierarchical weakly supervised learning for brain tumor segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 175\u2013183. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32248-9_20"},{"key":"47_CR11","doi-asserted-by":"crossref","unstructured":"Joulin, A., Bach, F., Ponce, J.: Discriminative clustering for image co-segmentation. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1943\u20131950, June 2010","DOI":"10.1109\/CVPR.2010.5539868"},{"key":"47_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/978-3-030-32226-7_44","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"S Joutard","year":"2019","unstructured":"Joutard, S., Dorent, R., Isaac, A., Ourselin, S., Vercauteren, T., Modat, M.: Permutohedral attention module for efficient non-local neural networks. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 393\u2013401. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32226-7_44"},{"key":"47_CR13","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"597","DOI":"10.1007\/978-3-319-59050-9_47","volume-title":"Information Processing in Medical Imaging","author":"K Kamnitsas","year":"2017","unstructured":"Kamnitsas, K., et al.: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, pp. 597\u2013609. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_47"},{"key":"47_CR14","unstructured":"Kr\u00e4henb\u00fchl, P., Koltun, V.: Efficient inference in fully connected crfs with gaussian edge potentials. In: Advances in Neural Information Processing Systems, vol. 24, pp. 109\u2013117. Curran Associates, Inc. (2011)"},{"issue":"1","key":"47_CR15","doi-asserted-by":"publisher","first-page":"6742","DOI":"10.1038\/s41598-019-43299-z","volume":"9","author":"K Kushibar","year":"2019","unstructured":"Kushibar, K., et al.: Supervised domain adaptation for automatic sub-cortical brain structure segmentation with minimal user interaction. Sci. Rep. 9(1), 6742 (2019)","journal-title":"Sci. Rep."},{"key":"47_CR16","doi-asserted-by":"crossref","unstructured":"Lin, D., Dai, J., Jia, J., He, K., Sun, J.: Scribblesup: scribble-supervised convolutional networks for semantic segmentation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016","DOI":"10.1109\/CVPR.2016.344"},{"issue":"12","key":"47_CR17","doi-asserted-by":"publisher","first-page":"2572","DOI":"10.1109\/TMI.2018.2842767","volume":"37","author":"F Mahmood","year":"2018","unstructured":"Mahmood, F., Chen, R., Durr, N.J.: Unsupervised reverse domain adaptation for synthetic medical images via adversarial training. IEEE Trans. Med. Imaging 37(12), 2572\u20132581 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"47_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/978-3-030-33391-1_7","volume-title":"Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data","author":"M Orbes-Arteaga","year":"2019","unstructured":"Orbes-Arteaga, M., et al.: Multi-domain adaptation in brain MRI through paired consistency and adversarial learning. In: Wang, Q., et al. (eds.) DART\/MIL3ID -2019. LNCS, vol. 11795, pp. 54\u201362. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33391-1_7"},{"key":"47_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neuroimage.2019.03.026","volume":"194","author":"CS Perone","year":"2019","unstructured":"Perone, C.S., Ballester, P., Barros, R.C., Cohen-Adad, J.: Unsupervised domain adaptation for medical imaging segmentation with self-ensembling. NeuroImage 194, 1\u201311 (2019)","journal-title":"NeuroImage"},{"key":"47_CR20","doi-asserted-by":"crossref","unstructured":"P\u00e9rez-Garc\u00eda, F., Sparks, R., Ourselin, S.: Torchio: a python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning (2020)","DOI":"10.1016\/j.cmpb.2021.106236"},{"key":"47_CR21","first-page":"1","volume":"1","author":"J Shapey","year":"2019","unstructured":"Shapey, J., et al.: An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced t1-weighted and high-resolution t2-weighted MRI. J. Neurosurg. JNS 1, 1\u20139 (2019)","journal-title":"J. Neurosurg. JNS"},{"key":"47_CR22","doi-asserted-by":"crossref","unstructured":"Tang, M., Perazzi, F., Djelouah, A., Ben Ayed, I., Schroers, C., Boykov, Y.: On regularized losses for weakly-supervised CNN segmentation. In: The European Conference on Computer Vision (ECCV), September 2018","DOI":"10.1007\/978-3-030-01270-0_31"},{"key":"47_CR23","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Adversarial discriminative domain adaptation. In: Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.316"},{"issue":"7","key":"47_CR24","doi-asserted-by":"publisher","first-page":"1562","DOI":"10.1109\/TMI.2018.2791721","volume":"37","author":"G Wang","year":"2018","unstructured":"Wang, G., et al.: Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans. Med. Imaging 37(7), 1562\u20131573 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"07","key":"47_CR25","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1109\/TPAMI.2018.2840695","volume":"41","author":"G Wang","year":"2019","unstructured":"Wang, G., et al.: Deepigeos: a deep interactive geodesic framework for medical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 41(07), 1559\u20131572 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"47_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1007\/978-3-030-32245-8_30","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"G Wang","year":"2019","unstructured":"Wang, G., et al.: Automatic segmentation of vestibular schwannoma from t2-weighted MRI by deep spatial attention with hardness-weighted loss. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 264\u2013272. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_30"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59710-8_47","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:09:55Z","timestamp":1759356595000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59710-8_47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597092","9783030597108"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59710-8_47","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.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":"1809","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":"542","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":"30% - 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 due to the COVID-19 pandemic.","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)"}}]}}