{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T15:04:29Z","timestamp":1767625469451,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164422"},{"type":"electronic","value":"9783031164439"}],"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_31","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T09:30:11Z","timestamp":1663234211000},"page":"319-329","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Automatic Identification of\u00a0Segmentation Errors for\u00a0Radiotherapy Using Geometric Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3752-4054","authenticated-orcid":false,"given":"Edward G. A.","family":"Henderson","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8297-0953","authenticated-orcid":false,"given":"Andrew F.","family":"Green","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6448-898X","authenticated-orcid":false,"given":"Marcel","family":"van Herk","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0741-994X","authenticated-orcid":false,"given":"Eliana M.","family":"Vasquez Osorio","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"31_CR1","doi-asserted-by":"publisher","unstructured":"Brouwer, C.L., et al.: 3D variation in delineation of head and neck organs at risk. Radiat. Oncol. 7(1) (2012). https:\/\/doi.org\/10.1186\/1748-717X-7-32","DOI":"10.1186\/1748-717X-7-32"},{"issue":"3","key":"31_CR2","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.semradonc.2019.02.001","volume":"29","author":"CE Cardenas","year":"2019","unstructured":"Cardenas, C.E., Yang, J., Anderson, B.M., Court, L.E., Brock, K.B.: Advances in auto-segmentation. Semin. Radiat. Oncol. 29(3), 185\u2013197 (2019). https:\/\/doi.org\/10.1016\/j.semradonc.2019.02.001","journal-title":"Semin. Radiat. Oncol."},{"key":"31_CR3","doi-asserted-by":"publisher","unstructured":"Chen, H.C., et al.: Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: a general strategy. Med. Phys. 42(2), 1048\u20131059 (2015). https:\/\/doi.org\/10.1118\/1.4906197","DOI":"10.1118\/1.4906197"},{"issue":"6","key":"31_CR4","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1109\/34.927467","volume":"23","author":"T Cootes","year":"2001","unstructured":"Cootes, T., Edwards, G., Taylor, C.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681\u2013685 (2001). https:\/\/doi.org\/10.1109\/34.927467","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"31_CR5","doi-asserted-by":"publisher","unstructured":"Fey, M., Lenssen, J.E.: Fast graph representation learning with PyTorch geometric. In: ICLR Workshop on Representation Learning on Graphs and Manifolds (2019). https:\/\/doi.org\/10.48550\/arXiv.1903.02428","DOI":"10.48550\/arXiv.1903.02428"},{"key":"31_CR6","doi-asserted-by":"publisher","unstructured":"Fey, M., Lenssen, J.E., Weichert, F., Muller, H.: SplineCNN: fast geometric deep learning with continuous B-spline kernels. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 869\u2013877. IEEE Computer Society, November 2018. https:\/\/doi.org\/10.1109\/CVPR.2018.00097","DOI":"10.1109\/CVPR.2018.00097"},{"issue":"3","key":"31_CR7","doi-asserted-by":"publisher","first-page":"e130","DOI":"10.1016\/j.clon.2021.11.008","volume":"34","author":"AF Green","year":"2022","unstructured":"Green, A.F., Aznar, M.C., Muirhead, R., Vasquez Osorio, E.M.: Reading the mind of a machine: hopes and hypes of artificial intelligence for clinical oncology imaging. Clin. Oncol. 34(3), e130\u2013e134 (2022). https:\/\/doi.org\/10.1016\/j.clon.2021.11.008","journal-title":"Clin. Oncol."},{"key":"31_CR8","doi-asserted-by":"publisher","unstructured":"Hui, C.B., et al.: Quality assurance tool for organ at risk delineation in radiation therapy using a parametric statistical approach. Med. Phys. 45(5), 2089\u20132096 (2018). https:\/\/doi.org\/10.1002\/mp.12835","DOI":"10.1002\/mp.12835"},{"key":"31_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1007\/978-3-642-33415-3_65","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2012","author":"T Kohlberger","year":"2012","unstructured":"Kohlberger, T., Singh, V., Alvino, C., Bahlmann, C., Grady, L.: Evaluating segmentation error without ground truth. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 528\u2013536. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33415-3_65"},{"key":"31_CR10","doi-asserted-by":"publisher","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (ICLR) (2019). https:\/\/doi.org\/10.48550\/arXiv.1711.05101","DOI":"10.48550\/arXiv.1711.05101"},{"issue":"6","key":"31_CR11","doi-asserted-by":"publisher","first-page":"3160","DOI":"10.1002\/mp.12304","volume":"44","author":"R McCarroll","year":"2017","unstructured":"McCarroll, R., et al.: Machine learning for the prediction of physician edits to clinical autocontours in the head-and-neck. Med. Phys. 44(6), 3160 (2017). https:\/\/doi.org\/10.1002\/mp.12304","journal-title":"Med. Phys."},{"issue":"12","key":"31_CR12","doi-asserted-by":"publisher","first-page":"3868","DOI":"10.1109\/TMI.2020.3006437","volume":"39","author":"A Mehrtash","year":"2020","unstructured":"Mehrtash, A., Wells, W.M., Tempany, C.M., Abolmaesumi, P., Kapur, T.: Confidence calibration and predictive uncertainty estimation for deep medical image segmentation. IEEE Trans. Med. Imaging 39(12), 3868\u20133878 (2020). https:\/\/doi.org\/10.1109\/TMI.2020.3006437","journal-title":"IEEE Trans. Med. Imaging"},{"key":"31_CR13","doi-asserted-by":"publisher","unstructured":"Men, K., Geng, H., Biswas, T., Liao, Z., Xiao, Y.: Automated quality assurance of OAR contouring for lung cancer based on segmentation with deep active learning. Front. Oncol. 10, 986 (2020). https:\/\/doi.org\/10.3389\/fonc.2020.00986","DOI":"10.3389\/fonc.2020.00986"},{"key":"31_CR14","doi-asserted-by":"publisher","unstructured":"Nikolov, S., et al.: Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy. ArXiv e-prints (2018). https:\/\/doi.org\/10.48550\/arXiv.1809.04430","DOI":"10.48550\/arXiv.1809.04430"},{"key":"31_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-319-46466-4_5","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Noroozi","year":"2016","unstructured":"Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving Jigsaw puzzles. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 69\u201384. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_5"},{"key":"31_CR16","doi-asserted-by":"publisher","unstructured":"Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., Battaglia, P.W.: Learning mesh-based simulation with graph networks. In: 9th International Conference on Learning Representations, ICLR (2021). https:\/\/doi.org\/10.48550\/arXiv.2010.03409","DOI":"10.48550\/arXiv.2010.03409"},{"key":"31_CR17","doi-asserted-by":"publisher","unstructured":"Rhee, D.J., et al.: Automatic detection of contouring errors using convolutional neural networks. Med. Phys. 46(11), 5086\u20135097 (2019). https:\/\/doi.org\/10.1002\/mp.13814","DOI":"10.1002\/mp.13814"},{"key":"31_CR18","doi-asserted-by":"publisher","unstructured":"Sander, J., de Vos, B.D., I\u0161gum, I.: Automatic segmentation with detection of local segmentation failures in cardiac MRI. Sci. Rep. 10(1), 1\u201319 (2020). https:\/\/doi.org\/10.1038\/s41598-020-77733-4","DOI":"10.1038\/s41598-020-77733-4"},{"key":"31_CR19","doi-asserted-by":"publisher","unstructured":"Taubin, G.: Curve and surface smoothing without shrinkage. In: Proceedings of IEEE International Conference on Computer Vision. IEEE Computer Society Press (1995). https:\/\/doi.org\/10.1109\/iccv.1995.466848","DOI":"10.1109\/iccv.1995.466848"},{"key":"31_CR20","doi-asserted-by":"publisher","unstructured":"Valindria, V.V., et al.: Reverse classification accuracy: predicting segmentation performance in the absence of ground truth. IEEE Trans. Med. Imaging 36(8), 1597\u20131606 (2017). https:\/\/doi.org\/10.1109\/TMI.2017.2665165","DOI":"10.1109\/TMI.2017.2665165"},{"key":"31_CR21","doi-asserted-by":"publisher","unstructured":"Vandewinckele, L., et al.: Overview of artificial intelligence-based applications in radiotherapy: recommendations for implementation and quality assurance (2020). https:\/\/doi.org\/10.1016\/j.radonc.2020.09.008","DOI":"10.1016\/j.radonc.2020.09.008"},{"key":"31_CR22","unstructured":"Vasquez Osorio, E.M., Shortall, J., Robbins, J., Van Herk, M.: Contour generation with realistic inter-observer variation. In: 19th International Conference on the use of Computers in Radiation Therapy, pp. 222\u2013223 (2019)"},{"key":"31_CR23","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=ryGs6iA5Km"},{"key":"31_CR24","doi-asserted-by":"publisher","unstructured":"Yan, Y., Yang, J., Li, Y., Ding, Y., Kadbi, M., Wang, J.: Impact of geometric distortion on dose deviation for photon and proton treatment plans. J. Appl. Clin. Med. Phys. 23(3) (2022). https:\/\/doi.org\/10.1002\/acm2.13517","DOI":"10.1002\/acm2.13517"},{"key":"31_CR25","doi-asserted-by":"publisher","unstructured":"Zhou, Q.Y., Park, J., Koltun, V.: Open3D: a modern library for 3D data processing. arXiv:1801.09847 (2018). https:\/\/doi.org\/10.48550\/arXiv.1801.09847","DOI":"10.48550\/arXiv.1801.09847"}],"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_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T16:46:15Z","timestamp":1709829975000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16443-9_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164422","9783031164439"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16443-9_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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)"}}]}}