{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T05:24:58Z","timestamp":1769750698616,"version":"3.49.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T00:00:00Z","timestamp":1706054400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T00:00:00Z","timestamp":1706054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61771322"],"award-info":[{"award-number":["61771322"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871186"],"award-info":[{"award-number":["61871186"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017607","name":"Shenzhen Fundamental Research Program","doi-asserted-by":"publisher","award":["JCYJ20190808160815125"],"award-info":[{"award-number":["JCYJ20190808160815125"]}],"id":[{"id":"10.13039\/501100017607","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s12559-023-10239-z","type":"journal-article","created":{"date-parts":[[2024,1,24]],"date-time":"2024-01-24T14:14:08Z","timestamp":1706105648000},"page":"1125-1140","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["MFCTrans: Multi-scale Feature Connection Transformer for Deformable Medical Image Registration"],"prefix":"10.1007","volume":"16","author":[{"given":"Longji","family":"Wang","sequence":"first","affiliation":[]},{"given":"Zhiyue","family":"Yan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8174-6167","authenticated-orcid":false,"given":"Wenming","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Jianhua","family":"Ji","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,24]]},"reference":[{"key":"10239_CR1","doi-asserted-by":"crossref","unstructured":"Sotiras A, Davatzikos C, Paragios N. Deformable medical image registration: a survey. IEEE Trans Med Imaging. 2013;32(7):1153\u20131190.","DOI":"10.1109\/TMI.2013.2265603"},{"key":"10239_CR2","doi-asserted-by":"crossref","unstructured":"Sayah MM, Redouane KM, Amine K. Stationary, continuous, and discrete wavelet-based approach for secure medical image transmission. Research on Biomedical Engineering. 2023;39(1):167\u201378.","DOI":"10.1007\/s42600-023-00261-3"},{"issue":"5","key":"10239_CR3","doi-asserted-by":"publisher","first-page":"7901","DOI":"10.1007\/s11042-022-13649-7","volume":"82","author":"K Amine","year":"2023","unstructured":"Amine K, Redouane K, Bilel M. A redundant wavelet based medical image watermarking scheme for secure transmission in telemedicine applications. Multimed Tools Appl. 2023;82(5):7901\u201315.","journal-title":"Multimed Tools Appl"},{"key":"10239_CR4","doi-asserted-by":"crossref","unstructured":"Gerig T, Shahim K, Reyes M, Vetter T, L\u00fcthi M. Spatially varying registration using gaussian processes. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2014. p. 413\u201320.","DOI":"10.1007\/978-3-319-10470-6_52"},{"issue":"1","key":"10239_CR5","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/LGRS.2014.2325970","volume":"12","author":"Y Wu","year":"2014","unstructured":"Wu Y, Ma W, Gong M, Su L, Jiao L. A novel point-matching algorithm based on fast sample consensus for image registration. IEEE Geosci Remote Sens Lett. 2014;12(1):43\u20137.","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10239_CR6","unstructured":"Luo W, Li Y, Urtasun R, Zemel R. Understanding the effective receptive field in deep convolutional neural networks. Adv Neural Inf Proces Syst. 2016;29."},{"key":"10239_CR7","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A. V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV). IEEE; 2016. p. 565\u201371.","DOI":"10.1109\/3DV.2016.79"},{"key":"10239_CR8","doi-asserted-by":"publisher","first-page":"109024","DOI":"10.1016\/j.patcog.2022.109024","volume":"133","author":"Z Li","year":"2023","unstructured":"Li Z, Hu Z, Luo W, Hu X. Sabernet: self-attention based effective relation network for few-shot learning. Pattern Recogn. 2023;133:109024.","journal-title":"Pattern Recogn."},{"key":"10239_CR9","doi-asserted-by":"crossref","unstructured":"Chang Q, Zhu S. Human vision attention mechanism-inspired temporal-spatial feature pyramid for video saliency detection. Cogn Comput. 2023;1\u201313.","DOI":"10.1007\/s12559-023-10114-x"},{"key":"10239_CR10","doi-asserted-by":"crossref","unstructured":"Chen J, He Y, Frey EC, Li Y, Du Y. Vit-v-net: vision transformer for unsupervised volumetric medical image registration. arXiv:2104.06468 [Preprint]. 2021.","DOI":"10.1016\/j.media.2022.102615"},{"key":"10239_CR11","doi-asserted-by":"publisher","first-page":"102615","DOI":"10.1016\/j.media.2022.102615","volume":"82","author":"J Chen","year":"2022","unstructured":"Chen J, Frey EC, He Y, Segars WP, Li Y, Du Y. Transmorph: transformer for unsupervised medical image registration. Med Image Anal. 2022;82:102615.","journal-title":"Med. Image Anal."},{"key":"10239_CR12","doi-asserted-by":"crossref","unstructured":"Ma M, Xu Y, Song L, Liu G. Symmetric transformer-based network for unsupervised image registration. Knowl-Based Syst. 2022;109959.","DOI":"10.1016\/j.knosys.2022.109959"},{"key":"10239_CR13","doi-asserted-by":"crossref","unstructured":"Zhou Z, Rahman\u00a0Siddiquee MM, Tajbakhsh N, Liang J. Unet++: a nested U-Net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4. Springer; 2018. p. 3\u201311.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"10239_CR14","doi-asserted-by":"crossref","unstructured":"Huang H, Lin L, Tong R, Hu H, Zhang Q, Iwamoto Y, Han X, Chen Y-W, Wu J. Unet 3+: a full-scale connected unet for medical image segmentation. In: ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE; 2020. p. 1055\u20139.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"10239_CR15","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"N Ibtehaz","year":"2020","unstructured":"Ibtehaz N, Rahman MS. Multiresunet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw. 2020;121:74\u201387.","journal-title":"Neural Netw."},{"issue":"2","key":"10239_CR16","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. Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int J Comput Vis. 2005;61(2):139\u201357.","journal-title":"Int. J. Comput. Vis."},{"issue":"1","key":"10239_CR17","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.media.2007.06.004","volume":"12","author":"BB Avants","year":"2008","unstructured":"Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal. 2008;12(1):26\u201341.","journal-title":"Med. Image Anal."},{"key":"10239_CR18","doi-asserted-by":"crossref","unstructured":"Wolberg G, Zokai S. Robust image registration using log-polar transform. In: Proceedings 2000 International Conference on Image Processing (Cat. No. 00CH37101), vol 1. IEEE; 2000. p. 493\u20136.","DOI":"10.1109\/ICIP.2000.901003"},{"issue":"2","key":"10239_CR19","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1023\/A:1007958904918","volume":"24","author":"P Viola","year":"1997","unstructured":"Viola P, Wells WM III. Alignment by maximization of mutual information. Int J Comput Vision. 1997;24(2):137\u201354.","journal-title":"Int. J. Comput. Vision"},{"issue":"1","key":"10239_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/S0734-189X(89)80014-3","volume":"46","author":"R Bajcsy","year":"1989","unstructured":"Bajcsy R, Kova\u010di\u010d S. Multiresolution elastic matching. Computer Vision, Graphics, and Image Processing. 1989;46(1):1\u201321.","journal-title":"Computer Vision, Graphics, and Image Processing"},{"issue":"8","key":"10239_CR21","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. Voxelmorph: a learning framework for deformable medical image registration. IEEE Trans Med Imaging. 2019;38(8):1788\u2013800.","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"10239_CR22","doi-asserted-by":"publisher","first-page":"2114","DOI":"10.1109\/TMI.2013.2274777","volume":"32","author":"DF Pace","year":"2013","unstructured":"Pace DF, Aylward SR, Niethammer M. A locally adaptive regularization based on anisotropic diffusion for deformable image registration of sliding organs. IEEE Trans Med Imaging. 2013;32(11):2114\u201326.","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"10239_CR23","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1109\/TMI.2016.2610583","volume":"36","author":"V Vishnevskiy","year":"2016","unstructured":"Vishnevskiy V, Gass T, Szekely G, Tanner C, Goksel O. Isotropic total variation regularization of displacements in parametric image registration. IEEE Trans Med Imaging. 2016;36(2):385\u201395.","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"2","key":"10239_CR24","doi-asserted-by":"publisher","first-page":"1363","DOI":"10.1007\/s10586-022-03686-0","volume":"26","author":"OA Alzubi","year":"2023","unstructured":"Alzubi OA, Qiqieh I, Alzubi JA. Fusion of deep learning based cyberattack detection and classification model for intelligent systems. Clust Comput. 2023;26(2):1363\u201374.","journal-title":"Clust. Comput."},{"key":"10239_CR25","doi-asserted-by":"crossref","unstructured":"Movassagh AA, Alzubi JA, Gheisari M, Rahimi M, Mohan S, Abbasi AA, Nabipour N. Artificial neural networks training algorithm integrating invasive weed optimization with differential evolutionary model. J Ambient Intell Humaniz Comput. 2021;1\u20139.","DOI":"10.1007\/s12652-020-02623-6"},{"key":"10239_CR26","doi-asserted-by":"crossref","unstructured":"Sokooti H, Vos BD, Berendsen F, Lelieveldt BP, I\u0161gum I, Staring M. Nonrigid image registration using multi-scale 3D convolutional neural networks. In: International Conference on Medical Image Computing and Computer-assisted Intervention. Springer; 2017. p. 232\u20139.","DOI":"10.1007\/978-3-319-66182-7_27"},{"key":"10239_CR27","doi-asserted-by":"crossref","unstructured":"Krebs J, Mansi T, Delingette H, Zhang L, Ghesu FC, Miao S, Maier AK, Ayache N, Liao R, Kamen A. Robust non-rigid registration through agent-based action learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2017. p. 344\u201352.","DOI":"10.1007\/978-3-319-66182-7_40"},{"key":"10239_CR28","unstructured":"Jaderberg M, Simonyan K, Zisserman A. et al. Spatial transformer networks. Adv Neural Inf Proces Syst. 2015;28."},{"key":"10239_CR29","unstructured":"Shan S, Yan W, Guo X, Chang EI, Fan Y, Xu Y, et al. Unsupervised end-to-end learning for deformable medical image registration. arXiv:1711.08608 [Preprint]. 2017."},{"key":"10239_CR30","doi-asserted-by":"crossref","unstructured":"Mok TC, Chung A. Large deformation diffeomorphic image registration with Laplacian pyramid networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2020. p. 211\u201321.","DOI":"10.1007\/978-3-030-59716-0_21"},{"key":"10239_CR31","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al. An image is worth 16x16 words: transformers for image recognition at scale. arXiv:2010.11929 [Preprint]. 2020."},{"key":"10239_CR32","doi-asserted-by":"crossref","unstructured":"Chen Z, Zheng Y, Gee JC. TransMatch: a transformer-based multilevel dual-stream feature matching network for unsupervised deformable image registration. IEEE Trans Med Imaging. 2023.","DOI":"10.1109\/TMI.2023.3288136"},{"key":"10239_CR33","doi-asserted-by":"crossref","unstructured":"Wang H, Cao P, Wang J, Zaiane OR. UCTransNet: rethinking the skip connections in U-Net from a channel-wise perspective with transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 36. 2022. p. 2441\u20139.","DOI":"10.1609\/aaai.v36i3.20144"},{"key":"10239_CR34","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser \u0141, Polosukhin I. Attention is all you need. Adv Neural Inf Proces Syst. 2017;30."},{"key":"10239_CR35","unstructured":"Dai Z, Liu H, Le QV, Tan M. CoAtNet: marrying convolution and attention for all data sizes. Adv Neural Inf Process Syst. 2021;34:3965\u201377."},{"key":"10239_CR36","unstructured":"Zhao Y, Wang G, Tang C, Luo C, Zeng W, Zha Z-J. A battle of network structures: an empirical study of CNN, transformer, and MLP. arXiv:2108.13002 [Preprint]. 2021."},{"key":"10239_CR37","doi-asserted-by":"crossref","unstructured":"Xie Y, Zhang J, Shen C, Xia Y. COTR: efficiently bridging CNN and transformer for 3D medical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention. Springer; 2021. p. 171\u201380.","DOI":"10.1007\/978-3-030-87199-4_16"},{"key":"10239_CR38","doi-asserted-by":"crossref","unstructured":"Zhang Y, Liu H, Hu Q. Transfuse: fusing transformers and CNNs for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2021. p. 14\u201324.","DOI":"10.1007\/978-3-030-87193-2_2"},{"key":"10239_CR39","doi-asserted-by":"crossref","unstructured":"Li C, Tang T, Wang G, Peng J, Wang B, Liang X, Chang X. BossNAS: exploring hybrid CNN-transformers with block-wisely self-supervised neural architecture search. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2021. p. 12281\u201391.","DOI":"10.1109\/ICCV48922.2021.01206"},{"key":"10239_CR40","doi-asserted-by":"crossref","unstructured":"Ding M, Xiao B, Codella N, Luo P, Wang J, Yuan L. DaViT: dual attention vision transformers. arXiv:2204.03645 [Preprint]. 2022.","DOI":"10.1007\/978-3-031-20053-3_5"},{"key":"10239_CR41","doi-asserted-by":"crossref","unstructured":"Qin Z, Zhang P, Wu F, Li X. FcaNet: frequency channel attention networks. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2021. p. 783\u201392.","DOI":"10.1109\/ICCV48922.2021.00082"},{"key":"10239_CR42","doi-asserted-by":"crossref","unstructured":"Balakrishnan G, Zhao A, Sabuncu MR, Guttag J, Dalca AV. An unsupervised learning model for deformable medical image registration. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018. p. 9252\u201360.","DOI":"10.1109\/CVPR.2018.00964"},{"key":"10239_CR43","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.media.2018.11.010","volume":"52","author":"BD De Vos","year":"2019","unstructured":"De Vos BD, Berendsen FF, Viergever MA, Sokooti H, Staring M, I\u0161gum I. A deep learning framework for unsupervised affine and deformable image registration. Med Image Anal. 2019;52:128\u201343.","journal-title":"Med. Image Anal."},{"key":"10239_CR44","doi-asserted-by":"crossref","unstructured":"Kim B, Kim J, Lee J-G, Kim DH, Park SH, Ye JC. Unsupervised deformable image registration using cycle-consistent CNN. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer; 2019. p. 166\u201374.","DOI":"10.1007\/978-3-030-32226-7_19"},{"issue":"9","key":"10239_CR45","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1162\/jocn.2007.19.9.1498","volume":"19","author":"DS Marcus","year":"2007","unstructured":"Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL. Open access series of imaging studies (oasis): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. J Cogn Neurosci. 2007;19(9):1498\u2013507.","journal-title":"J. Cogn. Neurosci."},{"issue":"3","key":"10239_CR46","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1016\/j.neuroimage.2007.09.031","volume":"39","author":"DW Shattuck","year":"2008","unstructured":"Shattuck DW, Mirza M, Adisetiyo V, Hojatkashani C, Salamon G, Narr KL, Poldrack RA, Bilder RM, Toga AW. Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage. 2008;39(3):1064\u201380.","journal-title":"Neuroimage"},{"issue":"2","key":"10239_CR47","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl B. FreeSurfer. Neuroimage. 2012;62(2):774\u201381.","journal-title":"Neuroimage"},{"issue":"3","key":"10239_CR48","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297\u2013302.","journal-title":"Ecology"},{"key":"10239_CR49","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L et al. PyTorch: an imperative style, high-performance deep learning library. Adv Neural Inf Proces Syst. 2019;32."}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-023-10239-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-023-10239-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-023-10239-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,9]],"date-time":"2024-11-09T01:20:43Z","timestamp":1731115243000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-023-10239-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,24]]},"references-count":49,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["10239"],"URL":"https:\/\/doi.org\/10.1007\/s12559-023-10239-z","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,24]]},"assertion":[{"value":"11 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}