{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T10:38:50Z","timestamp":1770892730225,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030208899","type":"print"},{"value":"9783030208905","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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-20890-5_33","type":"book-chapter","created":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T15:18:34Z","timestamp":1559402314000},"page":"511-526","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9841-8592","authenticated-orcid":false,"given":"Shanhui","family":"Sun","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0921-0592","authenticated-orcid":false,"given":"Jing","family":"Hu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5966-5440","authenticated-orcid":false,"given":"Mingqing","family":"Yao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7732-8141","authenticated-orcid":false,"given":"Jinrong","family":"Hu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0973-2537","authenticated-orcid":false,"given":"Xiaodong","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Song","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7659-1631","authenticated-orcid":false,"given":"Xi","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,6,2]]},"reference":[{"key":"33_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1007\/978-3-319-67558-9_24","volume-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","author":"BD Vos de","year":"2017","unstructured":"de Vos, B.D., Berendsen, F.F., Viergever, M.A., Staring, M., I\u0161gum, I.: End-to-end unsupervised deformable image registration with a convolutional neural network. In: Cardoso, M.J., et al. (eds.) DLMIA\/ML-CDS -2017. LNCS, vol. 10553, pp. 204\u2013212. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67558-9_24"},{"key":"33_CR2","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.neucom.2015.11.025","volume":"177","author":"C Zhao","year":"2016","unstructured":"Zhao, C., Zhao, H., Lv, J., Sun, S., Li, B.: Multimodal image matching based on multimodality robust line segment descriptor. Neurocomputing 177, 290\u2013303 (2016)","journal-title":"Neurocomputing"},{"key":"33_CR3","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1109\/TMI.2009.2028078","volume":"29","author":"S Liao","year":"2010","unstructured":"Liao, S., Chung, A.C.S.: Feature based nonrigid brain MR image registration with symmetric alpha stable filters. IEEE Trans. Med. Imaging 29, 106\u2013119 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"33_CR4","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1016\/j.jvcir.2013.06.010","volume":"27","author":"QR Razlighi","year":"2013","unstructured":"Razlighi, Q.R., Kehtarnavaz, N., Yousefi, S.: Evaluating similarity measures for brain image registration. J. Vis. Commun. Image Represent. 27, 977\u2013987 (2013)","journal-title":"J. Vis. Commun. Image Represent."},{"key":"33_CR5","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"key":"33_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/978-3-319-66182-7_26","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"H Uzunova","year":"2017","unstructured":"Uzunova, H., Wilms, M., Handels, H., Ehrhardt, J.: Training CNNs for image registration from few samples with model-based data augmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 223\u2013231. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_26"},{"key":"33_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1007\/978-3-319-46726-9_2","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","author":"M Simonovsky","year":"2016","unstructured":"Simonovsky, M., Guti\u00e9rrez-Becker, B., Mateus, D., Navab, N., Komodakis, N.: A deep metric for multimodal registration. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 10\u201318. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46726-9_2"},{"key":"33_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1007\/978-3-642-40763-5_80","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"G Wu","year":"2013","unstructured":"Wu, G., Kim, M., Wang, Q., Gao, Y., Liao, S., Shen, D.: Unsupervised deep feature learning for deformable registration of MR brain images. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 649\u2013656. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40763-5_80"},{"key":"33_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1007\/978-3-319-66182-7_27","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"H Sokooti","year":"2017","unstructured":"Sokooti, H., de Vos, B., Berendsen, F., Lelieveldt, B.P.F., I\u0161gum, I., Staring, M.: Nonrigid image registration using multi-scale 3D convolutional neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 232\u2013239. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_27"},{"key":"33_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1007\/978-3-319-66182-7_35","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"X Cao","year":"2017","unstructured":"Cao, X., et al.: Deformable image registration based on similarity-steered CNN regression. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 300\u2013308. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_35"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Liao, R., et al.: An artificial agent for robust image registration. In: AAAI, pp. 4168\u20134175 (2017)","DOI":"10.1609\/aaai.v31i1.11230"},{"key":"33_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1007\/978-3-319-66182-7_40","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"J Krebs","year":"2017","unstructured":"Krebs, J., et al.: Robust non-rigid registration through agent-based action learning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 344\u2013352. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_40"},{"key":"33_CR13","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529, 484\u2013489 (2016)","journal-title":"Nature"},{"key":"33_CR14","unstructured":"Bellver, M., Gir\u00f3-i Nieto, X., Marqu\u00e9s, F., Torres, J.: Hierarchical object detection with deep reinforcement learning. arXiv preprint arXiv:1611.03718 (2016)"},{"key":"33_CR15","doi-asserted-by":"crossref","unstructured":"Caicedo, J.C., Lazebnik, S.: Active object localization with deep reinforcement learning. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 2488\u20132496. IEEE (2015)","DOI":"10.1109\/ICCV.2015.286"},{"key":"33_CR16","unstructured":"Luo, W., Sun, P., Mu, Y., Liu, W.: End-to-end active object tracking via reinforcement learning. arXiv preprint arXiv:1705.10561 (2017)"},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Ren, Z., Wang, X., Zhang, N., Lv, X., Li, L.J.: Deep reinforcement learning-based image captioning with embedding reward. arXiv preprint arXiv:1704.03899 (2017)","DOI":"10.1109\/CVPR.2017.128"},{"key":"33_CR18","unstructured":"Tan, B., Xu, N., Kong, B.: Autonomous driving in reality with reinforcement learning and image translation. arXiv preprint arXiv:1801.05299 (2018)"},{"key":"33_CR19","unstructured":"Mnih, V., et al.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning, pp. 1928\u20131937 (2016)"},{"key":"33_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1007\/978-3-319-46976-8_6","volume-title":"Deep Learning and Data Labeling for Medical Applications","author":"X Yang","year":"2016","unstructured":"Yang, X., Kwitt, R., Niethammer, M.: Fast predictive image registration. In: Carneiro, G., et al. (eds.) LABELS\/DLMIA-2016. LNCS, vol. 10008, pp. 48\u201357. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46976-8_6"},{"key":"33_CR21","doi-asserted-by":"publisher","first-page":"1352","DOI":"10.1109\/TMI.2016.2521800","volume":"35","author":"S Miao","year":"2016","unstructured":"Miao, S., Wang, Z.J., Liao, R.: A CNN regression approach for real-time 2D\/3D registration. IEEE Trans. Med. Imaging 35, 1352\u20131363 (2016)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"33_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1007\/978-3-319-66182-7_28","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2017","author":"K Ma","year":"2017","unstructured":"Ma, K., et al.: Multimodal image registration with deep context reinforcement learning. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 240\u2013248. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_28"},{"key":"33_CR23","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.neucom.2012.09.034","volume":"125","author":"D Zhao","year":"2014","unstructured":"Zhao, D., Hu, Z., Xia, Z., Alippi, C., Zhu, Y., Wang, D.: Full-range adaptive cruise control based on supervised adaptive dynamic programming. Neurocomputing 125, 57\u201367 (2014)","journal-title":"Neurocomputing"},{"key":"33_CR24","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91\u2013110 (2004)","journal-title":"Int. J. Comput. Vis."},{"key":"33_CR25","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1109\/42.952729","volume":"20","author":"JM Fitzpatrick","year":"2001","unstructured":"Fitzpatrick, J.M., West, J.B.: The distribution of target registration error in rigid-body point-based registration. IEEE Trans. Med. Imaging 20, 917\u2013927 (2001)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"33_CR26","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1007\/11871842_29","volume-title":"Machine Learning: ECML 2006","author":"L Kocsis","year":"2006","unstructured":"Kocsis, L., Szepesv\u00e1ri, C.: Bandit based Monte-Carlo planning. In: F\u00fcrnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 282\u2013293. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11871842_29"},{"key":"33_CR27","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1109\/TMI.2009.2035616","volume":"29","author":"S Klein","year":"2010","unstructured":"Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.W.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29, 196\u2013205 (2010)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2018"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-20890-5_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T02:45:27Z","timestamp":1663555527000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-20890-5_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030208899","9783030208905"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-20890-5_33","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":"2 June 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Perth, WA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/accv2018.net\/","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"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"979","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"274","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"28% - 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"}},{"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"}},{"value":"2.7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}