{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T15:09:19Z","timestamp":1769612959599,"version":"3.49.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030322502","type":"print"},{"value":"9783030322519","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-32251-9_29","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"262-270","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":43,"title":["Multiple Landmark Detection Using Multi-agent Reinforcement Learning"],"prefix":"10.1007","author":[{"given":"Athanasios","family":"Vlontzos","sequence":"first","affiliation":[]},{"given":"Amir","family":"Alansary","sequence":"additional","affiliation":[]},{"given":"Konstantinos","family":"Kamnitsas","sequence":"additional","affiliation":[]},{"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[]},{"given":"Bernhard","family":"Kainz","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"29_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/978-3-030-00928-1_32","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"A Alansary","year":"2018","unstructured":"Alansary, A., et al.: Automatic view planning with multi-scale deep reinforcement learning agents. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 277\u2013285. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_32"},{"key":"29_CR2","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1016\/j.media.2019.02.007","volume":"53","author":"A Alansary","year":"2019","unstructured":"Alansary, A., et al.: Evaluating reinforcement learning agents for anatomical landmark detection. Med. Image Anal. 53, 156\u2013164 (2019)","journal-title":"Med. Image Anal."},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Bromley, J., Guyon, I., LeCun, Y., S\u00e4ckinger, E., Shah, R.: Signature verification using a \u201csiamese\u201d time delay neural network, pp. 737\u2013744 (1993)","DOI":"10.1142\/9789812797926_0003"},{"key":"29_CR4","unstructured":"Foerster, J., Assael, I.A., de Freitas, N., Whiteson, S.: Learning to communicate with deep multi-agent reinforcement learning. In: NIPS, vol. 29, pp. 2137\u20132145 (2016)"},{"key":"29_CR5","unstructured":"Foerster, J., Chen, R.Y., Al-Shedivat, M., Whiteson, S., Abbeel, P., Mordatch, I.: Learning with opponent-learning awareness. In: Proceedings of 17th International Conference on Autonomous Agents and MultiAgent Systems AAMAS 2018, pp. 122\u2013130 (2018)"},{"issue":"1","key":"29_CR6","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.media.2015.04.007","volume":"23","author":"R Gauriau","year":"2015","unstructured":"Gauriau, R., Cuingnet, R., Lesage, D., Bloch, I.: Multi-organ localization with cascaded global-to-local regression and shape prior. Med. Image Anal. 23(1), 70\u201383 (2015)","journal-title":"Med. Image Anal."},{"issue":"1","key":"29_CR7","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1109\/TPAMI.2017.2782687","volume":"41","author":"F Ghesu","year":"2019","unstructured":"Ghesu, F., Georgescu, B., Zheng, Y., Grbic, S., Maier, A., Hornegger, J., Comaniciu, D.: Multi-scale deep reinforcement learning for real-time 3D-landmark detection in CT scans. IEEE PAMI 41(1), 176\u2013189 (2019)","journal-title":"IEEE PAMI"},{"key":"29_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/978-3-319-46726-9_27","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"FC Ghesu","year":"2016","unstructured":"Ghesu, F.C., Georgescu, B., Mansi, T., Neumann, D., Hornegger, J., Comaniciu, D.: An artificial agent for anatomical landmark detection in medical images. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 229\u2013237. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46726-9_27"},{"key":"29_CR9","first-page":"223","volume":"39","author":"J Girard","year":"2015","unstructured":"Girard, J., Emami, R.: Concurrent Markov decision processes for robot team learning. EAAI 39, 223\u2013234 (2015)","journal-title":"EAAI"},{"key":"29_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1007\/978-3-319-71682-4_5","volume-title":"Autonomous Agents and Multiagent Systems","author":"JK Gupta","year":"2017","unstructured":"Gupta, J.K., Egorov, M., Kochenderfer, M.: Cooperative multi-agent control using deep reinforcement learning. In: Sukthankar, G., Rodriguez-Aguilar, J.A. (eds.) AAMAS 2017. LNCS (LNAI), vol. 10642, pp. 66\u201383. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-71682-4_5"},{"key":"29_CR11","unstructured":"Jaakkola, T., Singh, S.P., Jordan, M.I.: Reinforcement learning algorithm for partially observable Markov decision problems. In: NIPS (1995)"},{"issue":"4","key":"29_CR12","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1002\/jmri.21049","volume":"27","author":"CR Jack Jr","year":"2008","unstructured":"Jack Jr., C.R., et al.: The Alzheimer\u2019s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27(4), 685\u2013691 (2008)","journal-title":"J. Magn. Reson. Imaging"},{"key":"29_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1007\/978-3-030-00928-1_64","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","author":"Y Li","year":"2018","unstructured":"Li, Y., et al.: Fast multiple landmark localisation using a patch-based iterative network. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 563\u2013571. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00928-1_64"},{"issue":"1","key":"29_CR14","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/1532-429X-16-16","volume":"16","author":"A de Marvao","year":"2014","unstructured":"de Marvao, A., Dawes, T.J., Shi, W., Minas, C., Keenan, N.G., Diamond, T., Durighel, G., Montana, G., Rueckert, D., Cook, S.A., et al.: Population-based studies of myocardial hypertrophy: high resolution cardiovascular magnetic resonance atlases improve statistical power. J. Cardiovasc. Magn. Reson. 16(1), 16 (2014)","journal-title":"J. Cardiovasc. Magn. Reson."},{"key":"29_CR15","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 (2015)","journal-title":"Nature"},{"issue":"1","key":"29_CR16","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1109\/TMI.2016.2597270","volume":"36","author":"O Oktay","year":"2017","unstructured":"Oktay, O., et al.: Stratified decision forests for accurate anatomical landmark localization in cardiac images. IEEE Trans. Med. Imaging 36(1), 332\u2013342 (2017)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Rahmatullah, B., Papageorghiou, A.T., Noble, J.A.: Image analysis using machine learning: anatomical landmarks detection in fetal ultrasound images. In: 2012 IEEE 36th Annual Computer Software and Applications Conference, pp. 354\u2013355, July 2012","DOI":"10.1109\/COMPSAC.2012.52"},{"key":"29_CR18","unstructured":"Rashid, T., Samvelyan, M., de Witt, C.S., Farquhar, G., Foerster, J.N., Whiteson, S.: QMIX: monotonic value function factorisation for deep multi-agent reinforcement learning. CoRR abs\/1803.11485 (2018)"},{"key":"29_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1007\/978-3-319-24553-9_69","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"Y Zheng","year":"2015","unstructured":"Zheng, Y., Liu, D., Georgescu, B., Nguyen, H., Comaniciu, D.: 3D deep learning for efficient and robust landmark detection in volumetric data. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 565\u2013572. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24553-9_69"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32251-9_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:16:33Z","timestamp":1728519393000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32251-9_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322502","9783030322519"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32251-9_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 October 2019","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":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1730","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":"539","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":"3.07","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":"6.31","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}