{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,26]],"date-time":"2025-05-26T14:44:48Z","timestamp":1748270688448,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031164361"},{"type":"electronic","value":"9783031164378"}],"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\/978-3-031-16437-8_67","type":"book-chapter","created":{"date-parts":[[2022,9,15]],"date-time":"2022-09-15T18:13:04Z","timestamp":1663265584000},"page":"697-706","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Deep Reinforcement Learning for\u00a0Detection of\u00a0Inner Ear Abnormal Anatomy in\u00a0Computed Tomography"],"prefix":"10.1007","author":[{"given":"Paula","family":"L\u00f3pez Diez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristine","family":"S\u00f8rensen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Josefine Vilsb\u00f8ll","family":"Sundgaard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khassan","family":"Diab","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Margeta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fran\u00e7ois","family":"Patou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rasmus R.","family":"Paulsen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,16]]},"reference":[{"key":"67_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"718","DOI":"10.1007\/978-3-030-59713-9_69","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"C Baur","year":"2020","unstructured":"Baur, C., Graf, R., Wiestler, B., Albarqouni, S., Navab, N.: SteGANomaly: inhibiting CycleGAN steganography for unsupervised anomaly detection in brain MRI. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12262, pp. 718\u2013727. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59713-9_69"},{"key":"67_CR2","doi-asserted-by":"publisher","unstructured":"Baur, C., Wiestler, B., Muehlau, M., Zimmer, C., Navab, N., Albarqouni, S.: Modeling healthy anatomy with artificial intelligence for unsupervised anomaly detection in brain MRI. Radiol. Artif. Intell. 3(3), e190169 (2021). https:\/\/doi.org\/10.1148\/ryai.2021190169","DOI":"10.1148\/ryai.2021190169"},{"key":"67_CR3","doi-asserted-by":"publisher","unstructured":"Amor, L. B., Lahyani, I., Jmaiel, M.: PCA-based multivariate anomaly detection in mobile healthcare applications. In: Proceedings of the International Symposium on Distributed Simulation and Real Time Applications (DS-RT), pp. 1\u20138 (2017). https:\/\/doi.org\/10.1109\/DISTRA.2017.8167682","DOI":"10.1109\/DISTRA.2017.8167682"},{"key":"67_CR4","doi-asserted-by":"publisher","unstructured":"Cairo\/EG, R.Z.: Congenital inner ear abnormalities:a practical review. EPOS ECR 2019 \/ C-1911. https:\/\/doi.org\/10.26044\/ecr2019\/C-1911, https:\/\/dx.doi.org\/10.26044\/ecr2019\/C-1911","DOI":"10.26044\/ecr2019\/C-1911"},{"key":"67_CR5","unstructured":"Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: a survey (2019), http:\/\/arxiv.org\/abs\/1901.03407"},{"issue":"1","key":"67_CR6","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1006\/cviu.1995.1004","volume":"61","author":"TF Cootes","year":"1995","unstructured":"Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models-their training and application. Comput. Vis. Image Underst. 61(1), 38\u201359 (1995). https:\/\/doi.org\/10.1006\/cviu.1995.1004","journal-title":"Comput. Vis. Image Underst."},{"issue":"1","key":"67_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-00330-6","volume":"11","author":"A Dhanasingh","year":"2021","unstructured":"Dhanasingh, A., et al.: A novel method of identifying inner ear malformation types by pattern recognition in the mid modiolar section. Sci. Rep. 11(1), 1\u20139 (2021). https:\/\/doi.org\/10.1038\/s41598-021-00330-6","journal-title":"Sci. Rep."},{"key":"67_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1007\/978-3-030-00931-1_56","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"RS Gill","year":"2018","unstructured":"Gill, R.S., et al.: Deep convolutional networks for automated detection of epileptogenic brain malformations. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11072, pp. 490\u2013497. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00931-1_56"},{"issue":"1","key":"67_CR9","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/bf02291478","volume":"40","author":"JC Gower","year":"1975","unstructured":"Gower, J.C.: Generalized procrustes analysis. Psychometrika 40(1), 33\u201351 (1975). https:\/\/doi.org\/10.1007\/bf02291478","journal-title":"Psychometrika"},{"key":"67_CR10","doi-asserted-by":"publisher","unstructured":"Krenn, V.A., Fornai, C., Webb, N.M., Woodert, M.A., Prosch, H., Haeusler, M.: The morphological consequences of segmentation anomalies in the human sacrum. Am. J. Bio. Anthropol. 177(14), 690\u2013707 (2021). https:\/\/doi.org\/10.1002\/ajpa.24466, https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/ajpa.24466","DOI":"10.1002\/ajpa.24466"},{"key":"67_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/978-3-030-66843-3_18","volume-title":"Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology","author":"G Leroy","year":"2020","unstructured":"Leroy, G., Rueckert, D., Alansary, A.: Communicative reinforcement learning agents for landmark detection in brain images. In: MLCN\/RNO-AI -2020. LNCS, vol. 12449, pp. 177\u2013186. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-66843-3_18"},{"key":"67_CR12","doi-asserted-by":"publisher","unstructured":"Diez, P. L., et al.: Deep reinforcement learning for detection of abnormal anatomies. In: Proceedings of the Northern Lights Deep Learning Workshop, vol. 3 (2022). https:\/\/doi.org\/10.7557\/18.6280","DOI":"10.7557\/18.6280"},{"key":"67_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/978-3-030-87202-1_50","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2021","author":"P L\u00f3pez Diez","year":"2021","unstructured":"L\u00f3pez Diez, P., Sundgaard, J.V., Patou, F., Margeta, J., Paulsen, R.R.: Facial and cochlear nerves characterization using deep reinforcement learning for landmark detection. In: MICCAI 2021. LNCS, vol. 12904, pp. 519\u2013528. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_50"},{"key":"67_CR14","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih, V., et al.: Human-level control through deep reinforcement learning. Nature 518, 529\u2013533 (2015)","journal-title":"Nature"},{"issue":"1","key":"67_CR15","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/TMI.2019.2919951","volume":"39","author":"P Seeb\u00f6ck","year":"2020","unstructured":"Seeb\u00f6ck, P., et al.: Exploiting epistemic uncertainty of anatomy segmentation for anomaly detection in retinal oct. IEEE Trans. Med. Imaging 39(1), 87\u201398 (2020). https:\/\/doi.org\/10.1109\/TMI.2019.2919951","journal-title":"IEEE Trans. Med. Imaging"},{"key":"67_CR16","doi-asserted-by":"publisher","unstructured":"Sennarolu, L., Bajin, M.D.: Classification and current management of inner ear malformations. Balkan Med. J. 34 (2017). https:\/\/doi.org\/10.4274\/balkanmedj.2017.0367","DOI":"10.4274\/balkanmedj.2017.0367"},{"key":"67_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-030-00536-8_1","volume-title":"Simulation and Synthesis in Medical Imaging","author":"H-C Shin","year":"2018","unstructured":"Shin, H.-C., et al.: Medical image synthesis for data augmentation and anonymization using generative adversarial networks. In: Gooya, Ali, Goksel, Orcun, Oguz, Ipek, Burgos, Ninon (eds.) SASHIMI 2018. LNCS, vol. 11037, pp. 1\u201311. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00536-8_1"},{"key":"67_CR18","unstructured":"Trier, P., Noe, K. O., S\u00f8rensen, M.S., Mosegaard, J.: The visible ear surgery simulator, vol. 132 (2008)"},{"key":"67_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1007\/978-3-030-32251-9_29","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"A Vlontzos","year":"2019","unstructured":"Vlontzos, A., Alansary, A., Kamnitsas, K., Rueckert, D., Kainz, B.: Multiple landmark detection using multi-agent reinforcement learning. In: shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11767, pp. 262\u2013270. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32251-9_29"},{"issue":"3","key":"67_CR20","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","volume":"31","author":"PA Yushkevich","year":"2006","unstructured":"Yushkevich, P.A., et al.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116\u20131128 (2006). https:\/\/doi.org\/10.1016\/j.neuroimage.2006.01.015","journal-title":"Neuroimage"}],"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\/978-3-031-16437-8_67","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T14:11:51Z","timestamp":1710252711000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16437-8_67"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031164361","9783031164378"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16437-8_67","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)"}}]}}