{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T13:39:41Z","timestamp":1758893981428,"version":"3.37.3"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T00:00:00Z","timestamp":1625616000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T00:00:00Z","timestamp":1625616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771322","61871186"],"award-info":[{"award-number":["61771322","61871186"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Foundation of Shenzhen","award":["JCYJ20190808160815125."],"award-info":[{"award-number":["JCYJ20190808160815125."]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971290"],"award-info":[{"award-number":["61971290"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1007\/s00521-021-06243-9","type":"journal-article","created":{"date-parts":[[2021,7,7]],"date-time":"2021-07-07T11:02:52Z","timestamp":1625655772000},"page":"16471-16487","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A cascaded registration network RCINet with segmentation mask"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5182-5318","authenticated-orcid":false,"given":"Wenlan","family":"Zou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5488-4724","authenticated-orcid":false,"given":"Yi","family":"Luo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8174-6167","authenticated-orcid":false,"given":"Wenming","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Zhiquan","family":"He","sequence":"additional","affiliation":[]},{"given":"Zhihai","family":"He","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,7]]},"reference":[{"key":"6243_CR1","doi-asserted-by":"crossref","unstructured":"Fan J, Cao X, Xue Z, Yap P-T, Shen D (2018) Adversarial similarity network for evaluating image alignment in deep learning based registration. In: International conference on medical image computing and computer-assisted intervention. Springer, pp. 739\u2013746","DOI":"10.1007\/978-3-030-00928-1_83"},{"key":"6243_CR2","doi-asserted-by":"crossref","unstructured":"Sokooti H, De\u00a0Vos B, Berendsen F, Lelieveldt BPF, I\u0161gum I, Staring M (2017) Nonrigid image registration using multi-scale 3d convolutional neural networks. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 232\u2013239","DOI":"10.1007\/978-3-319-66182-7_27"},{"key":"6243_CR3","doi-asserted-by":"crossref","unstructured":"Roh\u00e9 M-M, Datar M, Heimann T, Sermesant M, Pennec X (2017) Svf-net: learning deformable image registration using shape matching. In: International conference on medical image computing and computer-assisted intervention. Springer, pp 266\u2013274","DOI":"10.1007\/978-3-319-66182-7_31"},{"key":"6243_CR4","doi-asserted-by":"crossref","unstructured":"Lu Z, Yang G, Hua T, Hu L, Kong Y, Tang L, Zhu X, Dillenseger J-L, Shu H, Coatrieux J-L (2019) Unsupervised three-dimensional image registration using a cycle convolutional neural network. In: 2019 IEEE international conference on image processing (ICIP). IEEE, pp 2174\u20132178","DOI":"10.1109\/ICIP.2019.8803163"},{"key":"6243_CR5","doi-asserted-by":"crossref","unstructured":"de\u00a0Vos BD, Berendsen FF, Viergever MA, Staring M, I\u0161gum I (2017) End-to-end unsupervised deformable image registration with a convolutional neural network. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, pp 204\u2013212","DOI":"10.1007\/978-3-319-67558-9_24"},{"issue":"8","key":"6243_CR6","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","volume":"38","author":"G Balakrishnan","year":"2019","unstructured":"Balakrishnan G, Zhao A, Sabuncu MR, Guttag J, Dalca AV (2019) Voxelmorph: a learning framework for deformable medical image registration. IEEE Trans Med Imaging 38(8):1788\u20131800","journal-title":"IEEE Trans Med Imaging"},{"key":"6243_CR7","unstructured":"Zhang J (2018) Inverse-consistent deep networks for unsupervised deformable image registration. arXiv preprint arXiv:1809.03443"},{"issue":"365","key":"6243_CR8","first-page":"1","volume":"2","author":"BB Avants","year":"2009","unstructured":"Avants BB, Tustison N, Song G (2009) Advanced normalization tools (ants). Insight J 2(365):1\u201335","journal-title":"Insight J"},{"issue":"1","key":"6243_CR9","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1109\/TMI.2009.2035616","volume":"29","author":"S Klein","year":"2009","unstructured":"Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW (2009) Elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 29(1):196\u2013205","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"6243_CR10","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s11263-009-0219-z","volume":"85","author":"M Hernandez","year":"2009","unstructured":"Hernandez M, Bossa MN, Olmos S (2009) Registration of anatomical images using paths of diffeomorphisms parameterized with stationary vector field flows. Int J Comput Vis 85(3):291\u2013306","journal-title":"Int J Comput Vis"},{"issue":"8","key":"6243_CR11","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1109\/83.855431","volume":"9","author":"SC Joshi","year":"2000","unstructured":"Joshi SC, Miller MI (2000) Landmark matching via large deformation diffeomorphisms. IEEE Trans Image Process 9(8):1357\u20131370","journal-title":"IEEE Trans Image Process"},{"issue":"27","key":"6243_CR12","doi-asserted-by":"publisher","first-page":"9685","DOI":"10.1073\/pnas.0503892102","volume":"102","author":"MI Miller","year":"2005","unstructured":"Miller MI, Beg MF, Ceritoglu C, Stark C (2005) Increasing the power of functional maps of the medial temporal lobe by using large deformation diffeomorphic metric mapping. Proc Natl Acad Sci 102(27):9685\u20139690","journal-title":"Proc Natl Acad Sci"},{"issue":"2","key":"6243_CR13","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","journal-title":"Int J Comput Vis"},{"key":"6243_CR14","doi-asserted-by":"crossref","unstructured":"Zhang M, Liao R, Dalca AV, Turk EA, Luo J, Grant PE, Golland P (2017) Frequency diffeomorphisms for efficient image registration. In: International conference on information processing in medical imaging. Springer, pp 559\u2013570","DOI":"10.1007\/978-3-319-59050-9_44"},{"issue":"9","key":"6243_CR15","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1109\/TMI.2005.853923","volume":"24","author":"Y Cao","year":"2005","unstructured":"Cao Y, Miller MI, Winslow RL, Younes L (2005) Large deformation diffeomorphic metric mapping of vector fields. IEEE Trans Med Imaging 24(9):1216\u20131230","journal-title":"IEEE Trans Med Imaging"},{"issue":"2","key":"6243_CR16","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1016\/j.neuroimage.2009.04.057","volume":"47","author":"C Ceritoglu","year":"2009","unstructured":"Ceritoglu C, Oishi K, Li X, Chou M-C, Younes L, Albert M, Lyketsos C, van Zijl PCM, Miller MI, Mori S (2009) Multi-contrast large deformation diffeomorphic metric mapping for diffusion tensor imaging. Neuroimage 47(2):618\u2013627","journal-title":"Neuroimage"},{"issue":"2","key":"6243_CR17","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1016\/j.neuroimage.2009.01.002","volume":"46","author":"K Oishi","year":"2009","unstructured":"Oishi K, Faria A, Jiang H, Li X, Akhter K, Zhang J, Hsu JT, Miller MI, van Zijl PCM, Albert M et al (2009) Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and alzheimer\u2019s disease participants. Neuroimage 46(2):486\u2013499","journal-title":"Neuroimage"},{"issue":"1","key":"6243_CR18","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.neuroimage.2007.07.007","volume":"38","author":"J Ashburner","year":"2007","unstructured":"Ashburner J (2007) A fast diffeomorphic image registration algorithm. Neuroimage 38(1):95\u2013113","journal-title":"Neuroimage"},{"issue":"1","key":"6243_CR19","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","journal-title":"NeuroImage"},{"key":"6243_CR20","doi-asserted-by":"crossref","unstructured":"Balakrishnan G, Zhao A, Sabuncu MR, Guttag J, Dalca AV (2018) An unsupervised learning model for deformable medical image registration. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 9252\u20139260","DOI":"10.1109\/CVPR.2018.00964"},{"key":"6243_CR21","unstructured":"Shan S, Yan W, Guo X, Chang EI, Fan Y, Xu Y, et\u00a0al (2017) Unsupervised end-to-end learning for deformable medical image registration. arXiv preprint arXiv:171108608"},{"key":"6243_CR22","doi-asserted-by":"crossref","unstructured":"Li H, Fan Y (2017) Non-rigid image registration using fully convolutional networks with deep self-supervision. arXiv preprint arXiv:170900799","DOI":"10.1109\/ISBI.2018.8363757"},{"key":"6243_CR23","unstructured":"Jaderberg M, Simonyan K, Zisserman A, et al (2015) Spatial transformer networks. In: Advances in neural information processing systems (NIPS), pp 2017\u20132025"},{"key":"6243_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2018.07.002","volume":"49","author":"Y Hu","year":"2018","unstructured":"Hu Y, Modat M, Gibson E, Li W, Ghavami N, Bonmati E, Wang G, Bandula S, Moore CM, Emberton M et al (2018) Weakly-supervised convolutional neural networks for multimodal image registration. Med Image Analysis 49:1\u201313","journal-title":"Med Image Analysis"},{"key":"6243_CR25","doi-asserted-by":"crossref","unstructured":"Hu Y, Modat M, Gibson E, Ghavami N, Bonmati E, Moore CM, Emberton M, Noble JA, Barratt DC, Vercauteren T (2018) Label-driven weakly-supervised learning for multimodal deformable image registration. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI). IEEE, pp 1070\u20131074","DOI":"10.1109\/ISBI.2018.8363756"},{"key":"6243_CR26","doi-asserted-by":"crossref","unstructured":"Zhao S, Dong Y, Chang EI, Xu Y et al (2019) Recursive cascaded networks for unsupervised medical image registration. In: Proceedings of the IEEE international conference on computer vision, pp 10600\u201310610","DOI":"10.1109\/ICCV.2019.01070"},{"key":"6243_CR27","doi-asserted-by":"crossref","unstructured":"Ali S, Rittscher J (2019) Conv2warp: An unsupervised deformable image registration with continuous convolution and warping. In: International workshop on machine learning in medical imaging. Springer, pp 489\u2013497","DOI":"10.1007\/978-3-030-32692-0_56"},{"issue":"5","key":"6243_CR28","doi-asserted-by":"publisher","first-page":"1394","DOI":"10.1109\/JBHI.2019.2951024","volume":"24","author":"S Zhao","year":"2019","unstructured":"Zhao S, Lau T, Luo J, Eric I, Chang C, Xu Y (2019) Unsupervised 3d end-to-end medical image registration with volume tweening network. IEEE J Biomed Health Informatics 24(5):1394\u20131404","journal-title":"IEEE J Biomed Health Informatics"},{"key":"6243_CR29","doi-asserted-by":"crossref","unstructured":"Cheng Z, Guo K, Wu C, Shen J, Qu L (2019) U-net cascaded with dilated convolution for medical image registration. In: 2019 Chinese automation congress (CAC). IEEE, pp 3647\u20133651","DOI":"10.1109\/CAC48633.2019.8996569"},{"issue":"1","key":"6243_CR30","doi-asserted-by":"publisher","first-page":"71","DOI":"10.2214\/ajr.174.1.1740071","volume":"174","author":"S Junji","year":"2000","unstructured":"Junji S, Shigehiko K, Junpei I, Tsuneo M, Takeshi K, Ken-ichi K, Mitate M, Hiroshi F, Yoshie K, Kunio D (2000) Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists\u2019 detection of pulmonary nodules. Am J Roentgenol 174(1):71\u201374","journal-title":"Am J Roentgenol"},{"issue":"2","key":"6243_CR31","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TMI.2013.2290491","volume":"33","author":"S Candemir","year":"2013","unstructured":"Candemir S, Jaeger S, Palaniappan K, Musco JP, Singh RK, Xue Z, Karargyris A, Antani S, Thoma G, McDonald CJ (2013) Lung segmentation in chest radiographs using anatomical atlases with nonrigid registration. IEEE Trans Med Imag 33(2):577\u2013590","journal-title":"IEEE Trans Med Imag"},{"issue":"2","key":"6243_CR32","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1109\/TMI.2013.2284099","volume":"33","author":"S Jaeger","year":"2013","unstructured":"Jaeger S, Karargyris A, Candemir S, Folio L, Siegelman J, Callaghan F, Xue Z, Palaniappan K, Singh RK, Antani S et al (2013) Automatic tuberculosis screening using chest radiographs. IEEE Trans Med Imag 33(2):233\u2013245","journal-title":"IEEE Trans Med Imag"},{"issue":"1\u20134","key":"6243_CR33","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/0167-2789(92)90242-F","volume":"60","author":"LI Rudin","year":"1992","unstructured":"Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Physica D Nonlinear Phenomena 60(1\u20134):259\u2013268","journal-title":"Physica D Nonlinear Phenomena"},{"key":"6243_CR34","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.neunet.2020.01.023","volume":"124","author":"L Mansilla","year":"2020","unstructured":"Mansilla L, Milone DH, Ferrante E (2020) Learning deformable registration of medical images with anatomical constraints. Neural Netw 124:269\u2013279","journal-title":"Neural Netw"},{"key":"6243_CR35","unstructured":"Chechik G, Shalit U, Sharma V, Bengio S (2009) An online algorithm for large scale image similarity learning. In: Advances in neural information processing systems (NIPS), pp 306\u2013314"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06243-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06243-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06243-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T18:18:23Z","timestamp":1635963503000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06243-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,7]]},"references-count":35,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["6243"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06243-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2021,7,7]]},"assertion":[{"value":"7 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We wish to draw the attention of the Editor to the following facts which may be considered as potential conflicts of interest and to significant financial contributions to this work. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing, we confirm that we have followed the regulations of our institutions concerning intellectual property. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}