{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:59:20Z","timestamp":1760147960977,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T00:00:00Z","timestamp":1679011200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61771230","ZR2021MF115"],"award-info":[{"award-number":["61771230","ZR2021MF115"]}]},{"name":"Shandong Provincial Natural Science Foundation","award":["61771230","ZR2021MF115"],"award-info":[{"award-number":["61771230","ZR2021MF115"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Deep-learning-based registration methods can not only save time but also automatically extract deep features from images. In order to obtain better registration performance, many scholars use cascade networks to realize a coarse-to-fine registration progress. However, such cascade networks will increase network parameters by an n-times multiplication factor and entail long training and testing stages. In this paper, we only use a cascade network in the training stage. Unlike others, the role of the second network is to improve the registration performance of the first network and function as an augmented regularization term in the whole process. In the training stage, the mean squared error loss function between the dense deformation field (DDF) with which the second network has been trained and the zero field is added to constrain the learned DDF such that it tends to 0 at each position and to compel the first network to conceive of a better deformation field and improve the network\u2019s registration performance. In the testing stage, only the first network is used to estimate a better DDF; the second network is not used again. The advantages of this kind of design are reflected in two aspects: (1) it retains the good registration performance of the cascade network; (2) it retains the time efficiency of the single network in the testing stage. The experimental results show that the proposed method effectively improves the network\u2019s registration performance compared to other state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/s23063208","type":"journal-article","created":{"date-parts":[[2023,3,17]],"date-time":"2023-03-17T03:33:57Z","timestamp":1679024037000},"page":"3208","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["The Successive Next Network as Augmented Regularization for Deformable Brain MR Image Registration"],"prefix":"10.3390","volume":"23","author":[{"given":"Meng","family":"Li","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Linyi University, Linyi 276000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1442-0976","authenticated-orcid":false,"given":"Shunbo","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Linyi University, Linyi 276000, China"}]},{"given":"Guoqiang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Linyi University, Linyi 276000, China"}]},{"given":"Fuchun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Linyi University, Linyi 276000, China"}]},{"given":"Jitao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Linyi University, Linyi 276000, China"}]},{"given":"Yue","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Linyi University, Linyi 276000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2234-9476","authenticated-orcid":false,"given":"Lintao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Linyi University, Linyi 276000, China"}]},{"given":"Mingtao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Linyi University, Linyi 276000, China"}]},{"given":"Yan","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Linyi University, Linyi 276000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2447-7650","authenticated-orcid":false,"given":"Deqian","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Linyi University, Linyi 276000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0370-448X","authenticated-orcid":false,"given":"Wenyin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Linyi University, Linyi 276000, China"}]},{"given":"Xing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Linyi University, Linyi 276000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2018.07.002","article-title":"Weakly-supervised convolutional neural networks for multimodal image registration","volume":"49","author":"Hu","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_2","unstructured":"Shan, S., Yan, W., Guo, X., Chang, E.I., Fan, Y., and Xu, Y. (2017). Unsupervised end-to-end learning for deformable medical image registration. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.media.2019.03.006","article-title":"BIRNet: Brain image registration using dual-supervised fully convolutional networks","volume":"54","author":"Fan","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1900","DOI":"10.1109\/TBME.2018.2822826","article-title":"Deformable image registration using a cue-aware deep regression network","volume":"65","author":"Cao","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, C., Yang, G., and Papanastasiou, G. (2022). Unsupervised image registration towards enhancing performance and explainability in cardiac and brain image analysis. Sensors, 22.","DOI":"10.3390\/s22062125"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","article-title":"VoxelMorph: A learning framework for deformable medical image registration","volume":"38","author":"Balakrishnan","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.media.2018.11.010","article-title":"A deep learning framework for unsupervised affine and deformable image registration","volume":"52","author":"Berendsen","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102036","DOI":"10.1016\/j.media.2021.102036","article-title":"CycleMorph: Cycle consistent unsupervised deformable image registration","volume":"71","author":"Kim","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kim, B., Kim, J., Lee, J.G., Kim, D.H., Park, S.H., and Ye, J.C. (2019, January 13\u201317). Unsupervised deformable image registration using cycle-consistent cnn. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Shenzhen, China.","DOI":"10.1007\/978-3-030-32226-7_19"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"015011","DOI":"10.1088\/1361-6560\/ab5da0","article-title":"A multi-scale framework with unsupervised joint training of convolutional neural networks for pulmonary deformable image registration","volume":"65","author":"Jiang","year":"2020","journal-title":"Phys. Med. Biol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kong, L., Yang, T., Xie, L., Xu, D., and He, K. (2022). Cascade connection-based channel attention network for bidirectional medical image registration. Vis. Comput., 1\u201319.","DOI":"10.1007\/s00371-022-02678-w"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2427","DOI":"10.1002\/mp.15515","article-title":"LDVoxelMorph: A precise loss function and cascaded architecture for unsupervised diffeomorphic large displacement registration","volume":"49","author":"Yang","year":"2022","journal-title":"Med. Phys."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104403","DOI":"10.1016\/j.bspc.2022.104403","article-title":"Similarity attention-based CNN for robust 3D medical image registration","volume":"81","author":"Zhu","year":"2023","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3315","DOI":"10.1109\/ACCESS.2020.3047829","article-title":"Preliminary feasibility study of imaging registration between supine and prone breast CT in breast cancer radiotherapy using residual recursive cascaded networks","volume":"9","author":"Ouyang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1002\/acm2.12968","article-title":"End-to-end unsupervised cycle-consistent fully convolutional network for 3D pelvic CT-MR deformable registration","volume":"21","author":"Guo","year":"2020","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sideri-Lampretsa, V., Kaissis, G., and Rueckert, D. (2022, January 28\u201331). Multi-modal unsupervised brain image registration using edge maps. Proceedings of the 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata, India.","DOI":"10.1109\/ISBI52829.2022.9761637"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"101887","DOI":"10.1016\/j.compmedimag.2021.101887","article-title":"A cascade-network framework for integrated registration of liver DCE-MR images","volume":"89","author":"Qian","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1364\/BOE.415939","article-title":"Hybrid Registration of Retinal Fluorescein Angiography and Optical Coherence Tomography Images of Patients with Diabetic Retinopathy","volume":"12","author":"Golkar","year":"2021","journal-title":"Biomed. Opt. Express"},{"key":"ref_19","first-page":"139","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","first-page":"321","article-title":"MedRegNet: Unsupervised multimodal retinal-image registration with GANs and ranking loss","volume":"Volume 12032","author":"Santarossa","year":"2022","journal-title":"Medical Imaging 2022: Image Processing"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fan, J., Cao, X., Xue, Z., Yap, P.T., and Shen, D. (2018, January 16\u201320). Adversarial similarity network for evaluating image alignment in deep learning based registration. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, Spain.","DOI":"10.1007\/978-3-030-00928-1_83"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"101545","DOI":"10.1016\/j.media.2019.101545","article-title":"Adversarial learning for mono-or multi-modal registration","volume":"58","author":"Fan","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_23","first-page":"5631","article-title":"SymReg-GAN: Symmetric image registration with generative adversarial networks","volume":"44","author":"Zheng","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yan, P.P., Xu, S., Rastinehad, A.R., and Wood, B.J. (2018, January 16). Adversarial image registration with application for MR and TRUS image fusion. Proceedings of the International Workshop on Machine Learning in Medical Imaging, Granada, Spain.","DOI":"10.1007\/978-3-030-00919-9_23"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101562","DOI":"10.1016\/j.bspc.2019.101562","article-title":"Adversarial learning for deformable registration of brain MR image using a multi-scale fully convolutional network","volume":"53","author":"Duan","year":"2019","journal-title":"Biomed. Signal Proces. Control"},{"key":"ref_26","unstructured":"Tanner, C., Ozdemir, F., Profanter, R., Vishnevsky, V., Konukoglu, E., and Goksel, O. (2018). Generative adversarial networks for MR-CT deformable image registration. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hu, Y., Gibson, E., Ghavami, N., Bonmati, E., Moore, C.M., Emberton, M., Vercauteren, T., Noble, J.A., and Barrat, D.C. (2018, January 16\u201320). Adversarial deformation regularization for training image registration neural networks. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, Spain.","DOI":"10.1007\/978-3-030-00928-1_87"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"085003","DOI":"10.1088\/1361-6560\/ab79c4","article-title":"4D-CT deformable image registration using multiscale unsupervised deep learning","volume":"65","author":"Lei","year":"2020","journal-title":"Phys. Med. Biol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101817","DOI":"10.1016\/j.media.2020.101817","article-title":"Difficulty-aware hierarchical convolutional neural networks for deformable registration of brain MR images","volume":"67","author":"Huang","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wu, L., Hu, S., and Liu, C. (2021). Exponential-distance weights for reducing grid-like artifacts in patch-based medical image registration. Sensors, 21.","DOI":"10.3390\/s21217112"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"170181","DOI":"10.1038\/sdata.2017.181","article-title":"An open resource for transdiagnostic research in pediatric mental health and learning disorders","volume":"4","author":"Alexander","year":"2017","journal-title":"Sci. Data"},{"key":"ref_32","first-page":"27","article-title":"The Neuro Bureau Preprocessing Initiative: Open sharing of preprocessed neuroimaging data and derivatives","volume":"7","author":"Craddock","year":"2013","journal-title":"Front. Neuroinform."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, M., Wang, Y., Zhang, F., Li, G., Hu, S., and Wu, L. (2021, January 23\u201325). Deformable medical image registration based on unsupervised generative adversarial network integrating dual attention mechanisms. Proceedings of the 14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China.","DOI":"10.1109\/CISP-BMEI53629.2021.9624229"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3208\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:57:25Z","timestamp":1760122645000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/6\/3208"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,17]]},"references-count":34,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23063208"],"URL":"https:\/\/doi.org\/10.3390\/s23063208","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,3,17]]}}}