{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T11:05:24Z","timestamp":1774868724395,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["Grant : 82260362"],"award-info":[{"award-number":["Grant : 82260362"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2021ZD0111000"],"award-info":[{"award-number":["2021ZD0111000"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013142","name":"Key Research and Development Project of Hainan Province","doi-asserted-by":"publisher","award":["ZDYF2021SHFZ243"],"award-info":[{"award-number":["ZDYF2021SHFZ243"]}],"id":[{"id":"10.13039\/501100013142","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s10489-025-07078-w","type":"journal-article","created":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T14:04:16Z","timestamp":1769090656000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DG-Morph: dense convolutional and gated feature extraction network for multimodal 3D prostate MRI registration"],"prefix":"10.1007","volume":"56","author":[{"given":"Mengxing","family":"Huang","sequence":"first","affiliation":[]},{"given":"Zhihao","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Zehao","family":"Ni","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1911-6228","authenticated-orcid":false,"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Nana","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Uzair Aslam","family":"Bhatti","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhiming","family":"Bai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,22]]},"reference":[{"issue":"3","key":"7078_CR1","first-page":"209","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL et al (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer J Clinicians 71(3):209\u2013249","journal-title":"CA: A Cancer J Clinicians"},{"key":"7078_CR2","doi-asserted-by":"publisher","first-page":"296","DOI":"10.1007\/s10489-025-06232-8","volume":"55","author":"L Li","year":"2025","unstructured":"Li L, Li L, Zhang Y et al (2025) Cyclic deformable medical image registration with prompt: deep fusion of diffeomorphic and transformer methods. Appl Intell 55:296","journal-title":"Appl Intell"},{"key":"7078_CR3","doi-asserted-by":"crossref","unstructured":"Nie Q, Zhang X, Chen C et al (2025) Reparameterized multi-scale transformer for deformable retinal image registration. Mach Intell Res","DOI":"10.1007\/s11633-024-1525-1"},{"key":"7078_CR4","doi-asserted-by":"crossref","unstructured":"Duan T, Chen W, Ruan M et al (2025) Unsupervised deep learning-based medical image registration: a survey. Phys Med Biol 70(2)","DOI":"10.1088\/1361-6560\/ad9e69"},{"key":"7078_CR5","doi-asserted-by":"publisher","first-page":"1281332","DOI":"10.3389\/fnbot.2023.1281332","volume":"17","author":"L Chen","year":"2024","unstructured":"Chen L, Feng C, Ma Y et al (2024) A review of rigid point cloud registration based on deep learning. Front Neurorobot 17:1281332","journal-title":"Front Neurorobot"},{"key":"7078_CR6","doi-asserted-by":"publisher","first-page":"7878","DOI":"10.1007\/s10489-024-05585-w","volume":"54","author":"M Lajili","year":"2024","unstructured":"Lajili M, Belhachmi Z, Moakher M et al (2024) Unsupervised deep learning for geometric feature detection and multilevel-multimodal image registration. Appl Intell 54:7878\u20137896","journal-title":"Appl Intell"},{"key":"7078_CR7","doi-asserted-by":"crossref","unstructured":"Zhang R, Mo H, Wang J et al (2024) UTSRMorph: a unified transformer and superresolution network for unsupervised medical image registration. IEEE Trans Med Imaging","DOI":"10.1109\/TMI.2024.3467919"},{"key":"7078_CR8","doi-asserted-by":"crossref","unstructured":"Song X, Xu X, Zhang J et al (2025) Dino-Reg: efficient multimodal image registration with distilled features. IEEE Trans Med Imaging","DOI":"10.1109\/TMI.2025.3567247"},{"key":"7078_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102615","volume":"82","author":"J Chen","year":"2022","unstructured":"Chen J, Frey EC, He Y et al (2022) Transmorph: transformer for unsupervised medical image registration. Med Image Anal 82:102615","journal-title":"Med Image Anal"},{"key":"7078_CR10","unstructured":"Vaswani A (2017) Attention is all you need. Adv Neural Inf Process Syst"},{"key":"7078_CR11","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Nath V, Tang Y et al (2021) Swin unetr: swin transformers for semantic segmentation of brain tumors in MRI images. In: Proceedings on international MICCAI brainlesion workshop. Cham, Switzerland, pp 272\u2013284","DOI":"10.1007\/978-3-031-08999-2_22"},{"key":"7078_CR12","doi-asserted-by":"publisher","first-page":"105137","DOI":"10.1016\/j.dsp.2025.105137","volume":"161","author":"M Deng","year":"2025","unstructured":"Deng M, Yang F, Chen QR et al (2025) A multi-scale refinement corner detection algorithm based on Shi-Harris. Digital Signal Process 161:105137","journal-title":"Digital Signal Process"},{"issue":"12","key":"7078_CR13","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.3390\/bioengineering11121236","volume":"11","author":"D Pennati","year":"2024","unstructured":"Pennati D, Bocchi L (2024) Analysis of the relationship between scale invariant feature transform keypoint properties and their invariance to geometrical transformation applied to cone-beam computed tomography images. Bioengineering 11(12):1236","journal-title":"Bioengineering"},{"key":"7078_CR14","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.inffus.2023.02.026","volume":"95","author":"M Hu","year":"2023","unstructured":"Hu M, Sun B, Kang X et al (2023) Multiscale structural feature transform for multi-modal image matching. Information Fusion 95:341\u2013354","journal-title":"Information Fusion"},{"key":"7078_CR15","unstructured":"Qiu H, Qin C, Schuh A, Hammernik K, Rueckert D (2021) Learning diffeomorphic and modality-invariant registration using B-Splines. In: Proceedings of the medical imaging with deep learning, vol 143. L\u00fcbeck, Germany, pp 645\u2013664"},{"issue":"1","key":"7078_CR16","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1007\/s00138-020-01060-x","volume":"31","author":"G Haskins","year":"2020","unstructured":"Haskins G, Kruger U, Yan P (2020) Deep learning in medical image registration: a survey. Mach Vis Appl 31(1):8","journal-title":"Mach Vis Appl"},{"key":"7078_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106769","volume":"157","author":"X Liu","year":"2023","unstructured":"Liu X, Chen H, Yao C et al (2023) BTMF-GAN: a multi-modal MRI fusion generative adversarial network for brain tumors. Comput Biol Med 157:106769","journal-title":"Comput Biol Med"},{"issue":"1","key":"7078_CR18","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.neucom.2021.11.023","volume":"486","author":"D Sengupta","year":"2022","unstructured":"Sengupta D, Gupta P, Biswas A (2022) A survey on mutual information based medical image registration algorithms. Neurocomputing 486(1):174\u2013188","journal-title":"Neurocomputing"},{"issue":"1","key":"7078_CR19","first-page":"012003","volume":"3","author":"X Chen","year":"2021","unstructured":"Chen X, Diaz-Pinto A, Ravikumar N et al (2021) Deep learning in medical image registration. Progress Biomed Eng 3(1):012003","journal-title":"Progress Biomed Eng"},{"issue":"9","key":"7078_CR20","first-page":"5631","volume":"44","author":"Y Zheng","year":"2021","unstructured":"Zheng Y, Sui X, Jiang Y et al (2021) SymReg-GAN: symmetric image registration with generative adversarial networks. IEEE Trans Pattern Anal Mach Intell 44(9):5631\u20135646","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7078_CR21","doi-asserted-by":"crossref","unstructured":"Abed A, Akrout B, Amous I (2022) A novel deep convolutional neural network architecture for customer counting in the retail environment. In: International conference on intelligent systems and pattern recognition. Springer International Publishing, Cham, pp 327\u2013340","DOI":"10.1007\/978-3-031-08277-1_27"},{"issue":"12","key":"7078_CR22","doi-asserted-by":"publisher","first-page":"9052","DOI":"10.1109\/TPAMI.2024.3415112","volume":"46","author":"J Gui","year":"2024","unstructured":"Gui J, Chen T, Zhang J et al (2024) A survey on self-supervised learning: algorithms, applications, and future trends. IEEE Trans Pattern Anal Mach Intell 46(12):9052\u20139071","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"12","key":"7078_CR23","doi-asserted-by":"publisher","first-page":"8704","DOI":"10.1109\/TPAMI.2019.2918284","volume":"44","author":"G Huang","year":"2019","unstructured":"Huang G, Liu Z, Pleiss G et al (2019) Convolutional networks with dense connectivity. IEEE Trans Pattern Anal Mach Intell 44(12):8704\u20138716","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"7078_CR24","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1080\/2150704X.2019.1697001","volume":"11","author":"G Li","year":"2020","unstructured":"Li G, Zhang C, Lei R et al (2020) Hyperspectral remote sensing image classification using three-dimensional-squeeze-and-excitation-DenseNet (3D-SE-DenseNet). Remote Sensing Letters 11(2):195\u2013203","journal-title":"Remote Sensing Letters"},{"issue":"6","key":"7078_CR25","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark K, Vendt B, Smith K et al (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26(6):1045\u20131057","journal-title":"J Digit Imaging"},{"key":"7078_CR26","doi-asserted-by":"crossref","unstructured":"Adams LC, Makowski MR, Engel G et al (2022) Prostate158-An expert-annotated 3T MRI dataset and algorithm for prostate cancer detection. Comput Biol Med, 105817","DOI":"10.1016\/j.compbiomed.2022.105817"},{"key":"7078_CR27","doi-asserted-by":"crossref","unstructured":"Kocak B, Klontzas ME, Stanzione A et al (2025) Evaluation metrics in medical imaging AI: fundamentals, pitfalls, misapplications, and recommendations. In: European journal of radiology artificial intelligence, vol 2025, p 100030","DOI":"10.1016\/j.ejrai.2025.100030"},{"key":"7078_CR28","doi-asserted-by":"crossref","unstructured":"Liu Y, Chen J, Wei S et al (2024) On finite difference jacobian computation in deformable image registration. Int J Comput Vision, 1\u201311","DOI":"10.1007\/s11263-024-02047-1"},{"key":"7078_CR29","doi-asserted-by":"crossref","unstructured":"Arar M, Ginger Y, Danon D et al (2020) Unsupervised multi-modal image registration via geometry preserving image-to-image translation. In: Proceedings on ICCV, pp 13410\u201313419","DOI":"10.1109\/CVPR42600.2020.01342"},{"key":"7078_CR30","doi-asserted-by":"crossref","unstructured":"Mudeng V, Kim M, Choe S et al (2022) Prospects of structural similarity index for medical image analysis. Appl Sci, 3754","DOI":"10.3390\/app12083754"},{"issue":"1","key":"7078_CR31","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s11263-024-02186-5","volume":"133","author":"FA Croitoru","year":"2025","unstructured":"Croitoru FA, Ristea NC, Ionescu RT et al (2025) Learning rate curriculum. Int J Comput Vision 133(1):291\u2013314","journal-title":"Int J Comput Vision"},{"key":"7078_CR32","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1016\/j.media.2019.07.005","volume":"57","author":"D Karimi","year":"2019","unstructured":"Karimi D, Zeng Q, Mathur P et al (2019) Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images. Med Image Anal 57:186\u2013196","journal-title":"Med Image Anal"},{"key":"7078_CR33","doi-asserted-by":"crossref","unstructured":"Chen B, Wen M, Shi Y et al (2022) Towards training reproducible deep learning models. In: Proceedings on ICSE, pp 2202\u20132214","DOI":"10.1145\/3510003.3510163"},{"issue":"12","key":"7078_CR34","doi-asserted-by":"publisher","first-page":"15621","DOI":"10.1007\/s10489-022-04230-8","volume":"53","author":"OK Oyedotun","year":"2023","unstructured":"Oyedotun OK, Papadopoulos K, Aouada D (2023) A new perspective for understanding generalization gap of deep neural networks trained with large batch sizes. Appl Intell 53(12):15621\u201315637","journal-title":"Appl Intell"},{"issue":"365","key":"7078_CR35","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":"8","key":"7078_CR36","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 et al (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":"7078_CR37","unstructured":"Chen J, He Y, Frey EC, Li Y, Du Y (2021) Vit-v-net: vision transformer for unsupervised volumetric medical image registration. arXiv:2104.06468. https:\/\/arxiv.org\/abs\/2104.06468"},{"key":"7078_CR38","doi-asserted-by":"crossref","unstructured":"Shi J, He Y, Kong Y et al (2022) Xmorpher: full transformer for deformable medical image registration via cross attention. In: Proceedings on ICMICC. Cham, Switzerland, pp 217\u2013226","DOI":"10.1007\/978-3-031-16446-0_21"},{"key":"7078_CR39","doi-asserted-by":"crossref","unstructured":"Zhuo Y, Shen Y (2024) Diffusereg: denoising diffusion model for obtaining deformation fields in unsupervised deformable image registration. In: International conference on medical image computing and computer-assisted intervention. Cham, Springer Nature Switzerland, pp 597\u2013607","DOI":"10.1007\/978-3-031-72069-7_56"},{"key":"7078_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102036","volume":"71","author":"B Kim","year":"2021","unstructured":"Kim B, Kim DH, Park SH, Kim J, Lee J-G, Ye JC (2021) CycleMorph: cycle consistent unsupervised deformable image registration. Med Image Anal 71:102036","journal-title":"Med Image Anal"},{"key":"7078_CR41","doi-asserted-by":"crossref","unstructured":"Xin Y, Chen Y, Ji S et al (2024) On-the-fly guidance training for medical image registration. In: Proceedings on MICCAI, vol 15002. Cham, Switzerland, pp 694\u2013705","DOI":"10.1007\/978-3-031-72069-7_65"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-07078-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-07078-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-07078-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T10:16:34Z","timestamp":1774865794000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-07078-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":41,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["7078"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-07078-w","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"30 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2026","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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"53"}}