{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T13:16:25Z","timestamp":1740143785016,"version":"3.37.3"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T00:00:00Z","timestamp":1701388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2017A030310217"],"award-info":[{"award-number":["2017A030310217"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009334","name":"Pearl River S and T Nova Program of Guangzhou","doi-asserted-by":"publisher","award":["201710010162"],"award-info":[{"award-number":["201710010162"]}],"id":[{"id":"10.13039\/501100009334","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11517-023-02949-1","type":"journal-article","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T01:02:24Z","timestamp":1702515744000},"page":"3181-3191","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A multi-view assisted registration network for MRI registration pre- and post-therapy"],"prefix":"10.1007","volume":"61","author":[{"given":"Yanxia","family":"Liu","sequence":"first","affiliation":[]},{"given":"Xiaozhen","family":"Li","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[]},{"given":"SiJuan","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"issue":"3","key":"2949_CR1","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/S1361-8415(98)80022-4","volume":"2","author":"J Thirion","year":"1998","unstructured":"Thirion J (1998) Image matching as a diffusion process: an analogy with Maxwell\u2019s demons. Medical Image Anal 2(3):243\u2013260. https:\/\/doi.org\/10.1016\/S1361-8415(98)80022-4","journal-title":"Medical Image Anal"},{"issue":"12","key":"2949_CR2","doi-asserted-by":"publisher","first-page":"2879","DOI":"10.1109\/TIP.2007.909412","volume":"16","author":"S Klein","year":"2007","unstructured":"Klein S, Staring M, Pluim JPW (2007) Evaluation of optimization methods for nonrigid medical image registration using mutual information and b-splines. IEEE Trans Image Process 16(12):2879\u20132890. https:\/\/doi.org\/10.1109\/TIP.2007.909412","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"2949_CR3","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 MA, Pluim JPW (2010) elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 29(1):196\u2013205","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"2949_CR4","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1023\/B:VISI.0000043755.93987.aa","volume":"61","author":"MF Beg","year":"2005","unstructured":"Beg MF, Miller MI, Trouv\u00e9 A, Younes L (2005) Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int J Comput Vis 61(2):139\u2013157. https:\/\/doi.org\/10.1023\/B:VISI.0000043755.93987.aa","journal-title":"Int J Comput Vis"},{"issue":"3","key":"2949_CR5","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1016\/j.neuroimage.2010.09.025","volume":"54","author":"BB Avants","year":"2011","unstructured":"Avants BB, Tustison NJ, Song G, Cook PA, Klein A, Gee JC (2011) A reproducible evaluation of ants similarity metric performance in brain image registration. Neuroimage 54(3):2033\u20132044. https:\/\/doi.org\/10.1016\/j.neuroimage.2010.09.025","journal-title":"Neuroimage"},{"issue":"1","key":"2949_CR6","doi-asserted-by":"publisher","first-page":"S61","DOI":"10.1016\/j.neuroimage.2008.10.040","volume":"45","author":"T Vercauteren","year":"2009","unstructured":"Vercauteren T, Pennec X, Perchant A, Ayache N (2009) Diffeomorphic demons: efficient non-parametric image registration. Neuroimage 45(1):S61\u2013S72. https:\/\/doi.org\/10.1016\/j.neuroimage.2008.10.040","journal-title":"Neuroimage"},{"key":"2949_CR7","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/j.neuroimage.2013.04.114","volume":"81","author":"M Lorenzi","year":"2013","unstructured":"Lorenzi M, Ayache N, Frisoni GB, Pennec X (2013) LCC-demons: a robust and accurate symmetric diffeomorphic registration algorithm. Neuroimage 81:470\u2013483. https:\/\/doi.org\/10.1016\/j.neuroimage.2013.04.114","journal-title":"Neuroimage"},{"issue":"1","key":"2949_CR8","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1002\/ima.22801","volume":"33","author":"T Yang","year":"2023","unstructured":"Yang T, Bai X, Cui X, Gong Y, Li L (2023) DAU-net: an unsupervised 3d brain MRI registration model with dual-attention mechanism. Int J Imaging Syst Technol 33(1):217\u2013229. https:\/\/doi.org\/10.1002\/ima.22801","journal-title":"Int J Imaging Syst Technol"},{"key":"2949_CR9","unstructured":"Jaderberg M, Simonyan K, Zisserman A, Kavukcuoglu K (2015) Spatial transformer networks. CoRR abs\/1506.02025. http:\/\/arxiv.org\/abs\/1506.02025"},{"key":"2949_CR10","doi-asserted-by":"crossref","unstructured":"Balakrishnan G, Zhao A, Sabuncu MR, Guttag JV, Dalca AV (2018a) An unsupervised learning model for deformable medical image registration. In: 2018 IEEE Conference on computer vision and pattern recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018, computer vision foundation \/ IEEE computer society, pp 9252\u20139260","DOI":"10.1109\/CVPR.2018.00964"},{"key":"2949_CR11","doi-asserted-by":"publisher","unstructured":"Kuang D, Schmah T (2019) FAIM - A convnet method for unsupervised 3d medical image registration. In: Suk H, Liu M, Yan P, Lian C (eds) Machine learning in medical imaging - 10th international workshop, MLMI 2019, held in conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, proceedings, Springer, Lecture Notes in Computer Science, vol 11861, pp 646\u2013654. https:\/\/doi.org\/10.1007\/978-3-030-32692-0_74","DOI":"10.1007\/978-3-030-32692-0_74"},{"key":"2949_CR12","doi-asserted-by":"crossref","unstructured":"de\u00a0Vos BD, Berendsen FF, Viergever MA, Staring M, Isgum I (2017) End-to-end unsupervised deformable image registration with a convolutional neural network. CoRR abs\/1704.06065. http:\/\/arxiv.org\/abs\/1704.06065","DOI":"10.1007\/978-3-319-67558-9_24"},{"key":"2949_CR13","unstructured":"Zhang J (2018) Inverse-consistent deep networks for unsupervised deformable image registration. CoRR abs\/1809.03443. http:\/\/arxiv.org\/abs\/1809.03443"},{"key":"2949_CR14","doi-asserted-by":"crossref","unstructured":"Mok TCW, Chung ACS (2020a) Fast symmetric diffeomorphic image registration with convolutional neural networks. CoRR abs\/2003.09514. https:\/\/arxiv.org\/abs\/2003.09514","DOI":"10.1109\/CVPR42600.2020.00470"},{"key":"2949_CR15","doi-asserted-by":"publisher","unstructured":"Han R, Jones CK, Lee J, Wu P, Vagdargi P, Uneri A, Helm PA, Luciano M, Anderson WS, Siewerdsen JH (2022) Deformable MR-CT image registration using an unsupervised, dual-channel network for neurosurgical guidance. Medical Image Anal 75:102292. https:\/\/doi.org\/10.1016\/j.media.2021.102292","DOI":"10.1016\/j.media.2021.102292"},{"issue":"7","key":"2949_CR16","doi-asserted-by":"publisher","first-page":"2506","DOI":"10.1109\/TMI.2020.2972616","volume":"39","author":"T Fechter","year":"2020","unstructured":"Fechter T, Baltas D (2020) One-shot learning for deformable medical image registration and periodic motion tracking. IEEE Trans Medical Imaging 39(7):2506\u20132517. https:\/\/doi.org\/10.1109\/TMI.2020.2972616","journal-title":"IEEE Trans Medical Imaging"},{"key":"2949_CR17","doi-asserted-by":"publisher","unstructured":"Mok TCW, Chung ACS (2021) Conditional deformable image registration with convolutional neural network. In: de\u00a0Bruijne M, Cattin PC, Cotin S, Padoy N, Speidel S, Zheng Y, Essert C (eds) Medical image computing and computer assisted intervention - MICCAI 2021 - 24th international conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part IV, Springer, Lecture Notes in Computer Science, vol 12904, pp 35\u201345. https:\/\/doi.org\/10.1007\/978-3-030-87202-1_4","DOI":"10.1007\/978-3-030-87202-1_4"},{"key":"2949_CR18","doi-asserted-by":"publisher","unstructured":"Hering A, van Ginneken B, Heldmann S (2019) mlvirnet: multilevel variational image registration network. In: Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S, Yap P, Khan AR (eds) Medical image computing and computer assisted intervention - MICCAI 2019 - 22nd international conference, Shenzhen, China, October 13-17, 2019, Proceedings, Part VI, Springer, Lecture Notes in Computer Science, vol 11769, pp 257\u2013265. https:\/\/doi.org\/10.1007\/978-3-030-32226-7_29","DOI":"10.1007\/978-3-030-32226-7_29"},{"issue":"3","key":"2949_CR19","doi-asserted-by":"publisher","first-page":"2936","DOI":"10.1007\/s10489-022-03659-1","volume":"53","author":"Z He","year":"2023","unstructured":"He Z, He Y, Cao W (2023) Deformable image registration with attention-guided fusion of multi-scale deformation fields. Appl Intell 53(3):2936\u20132950. https:\/\/doi.org\/10.1007\/s10489-022-03659-1","journal-title":"Appl Intell"},{"key":"2949_CR20","doi-asserted-by":"publisher","unstructured":"Mok TCW, Chung ACS (2022) Unsupervised deformable image registration with absent correspondences in pre-operative and post-recurrence brain tumor MRI scans. In: Wang L, Dou Q, Fletcher PT, Speidel S, Li S (eds) Medical image computing and computer assisted intervention - MICCAI 2022 - 25th International Conference, Singapore, September 18-22, 2022, proceedings, Part VI, Springer, Lecture Notes in Computer Science, vol 13436, pp 25\u201335. https:\/\/doi.org\/10.1007\/978-3-031-16446-0_3","DOI":"10.1007\/978-3-031-16446-0_3"},{"key":"2949_CR21","doi-asserted-by":"crossref","unstructured":"Hu X, Kang M, Huang W, Scott MR, Wiest R, Reyes M (2019) Dual-stream pyramid registration network. CoRR abs\/1909.11966. http:\/\/arxiv.org\/abs\/1909.11966","DOI":"10.1007\/978-3-030-32245-8_43"},{"key":"2949_CR22","unstructured":"Mok TCW, Chung ACS (2020b) Large deformation diffeomorphic image registration with Laplacian pyramid networks. CoRR abs\/2006.16148. https:\/\/arxiv.org\/abs\/2006.16148"},{"issue":"2","key":"2949_CR23","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1007\/s11517-021-02317-x","volume":"59","author":"C Li","year":"2021","unstructured":"Li C, Zhou Y, Li Y, Yang S (2021) A coarse-to-fine registration method for three-dimensional MR images. Medical Biol Eng Comput 59(2):457\u2013469. https:\/\/doi.org\/10.1007\/s11517-021-02317-x","journal-title":"Medical Biol Eng Comput"},{"key":"2949_CR24","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. CoRR abs\/1505.04597","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2949_CR25","unstructured":"de\u00a0Vos BD, Berendsen FF, Viergever MA, Sokooti H, Staring M, Isgum I (2018) A deep learning framework for unsupervised affine and deformable image registration. CoRR abs\/1809.06130. http:\/\/arxiv.org\/abs\/1809.06130"},{"key":"2949_CR26","doi-asserted-by":"crossref","unstructured":"Balakrishnan G, Zhao A, Sabuncu MR, Guttag JV, Dalca AV (2018b) An unsupervised learning model for deformable medical image registration. CoRR abs\/1802.02604. http:\/\/arxiv.org\/abs\/1802.02604","DOI":"10.1109\/CVPR.2018.00964"},{"key":"2949_CR27","doi-asserted-by":"crossref","unstructured":"Kim B, Kim J, Lee J, Kim DH, Park SH, Ye JC (2019) Unsupervised deformable image registration using cycle-consistent CNN. CoRR abs\/1907.01319. http:\/\/arxiv.org\/abs\/1907.01319","DOI":"10.1007\/978-3-030-32226-7_19"},{"key":"2949_CR28","doi-asserted-by":"publisher","unstructured":"Berthilsson R (1998) Affine correlation. In: Jain AK, Venkatesh S, Lovell BC (eds) Fourteenth International Conference on Pattern Recognition, ICPR 1998, Brisbane, Australia, 16-20 August, 1998, IEEE Computer Society, pp 1458\u20131460. https:\/\/doi.org\/10.1109\/ICPR.1998.711979","DOI":"10.1109\/ICPR.1998.711979"},{"key":"2949_CR29","doi-asserted-by":"publisher","unstructured":"Shu Y, Wang H, Xiao B, Bi X, Li W (2021) Medical image registration based on uncoupled learning and accumulative enhancement. In: de\u00a0Bruijne M, Cattin PC, Cotin S, Padoy N, Speidel S, Zheng Y, Essert C (eds) Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27 - October 1, 2021, Proceedings, Part IV, Springer, Lecture Notes in Computer Science, vol 12904, pp 3\u201313. https:\/\/doi.org\/10.1007\/978-3-030-87202-1_1","DOI":"10.1007\/978-3-030-87202-1_1"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02949-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02949-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02949-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T03:18:32Z","timestamp":1703301512000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02949-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12]]},"references-count":29,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["2949"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02949-1","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"type":"print","value":"0140-0118"},{"type":"electronic","value":"1741-0444"}],"subject":[],"published":{"date-parts":[[2023,12]]},"assertion":[{"value":"11 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}