{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T05:06:48Z","timestamp":1787807208083,"version":"build-2784847793"},"reference-count":30,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T00:00:00Z","timestamp":1734393600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62471306"],"award-info":[{"award-number":["62471306"]}],"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":["62071305"],"award-info":[{"award-number":["62071305"]}],"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":["82071951"],"award-info":[{"award-number":["82071951"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Most existing deep learning\u2010based registration methods are trained on single\u2010type images to address same\u2010domain tasks, resulting in performance degradation when applied to new scenarios. Retraining a model for new scenarios requires extra time and data. Therefore, efficient and accurate solutions for cross\u2010domain deformable registration are in\u00a0demand.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>We argue that the tailor\u2010made matching criteria in traditional registration methods is one of the main reason they are applicable in different domains. Motivated by this, we devise a registration\u2010oriented encoder to model the matching criteria of image features and structural features, which is beneficial to boost registration accuracy and\u00a0adaptability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Specifically, a general feature encoder (Encoder\u2010G) is proposed to capture comprehensive medical image features, while a structural feature encoder (Encoder\u2010S) is designed to encode the structural self\u2010similarity into the global representation. Moreover, by updating Encoder\u2010S using one\u2010shot learning, our method can effectively adapt to different domains. The efficacy of our method is evaluated using MRI images from three different domains, including brain images (training\/testing: 870\/90 pairs), abdomen images (training\/testing: 1406\/90 pairs), and cardiac images (training\/testing: 64770\/870 pairs). The comparison methods include traditional method (SyN) and cutting\u2010edge deep networks. The evaluation metrics contain dice similarity coefficient (DSC) and average symmetric surface distance (ASSD).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In the single\u2010domain task, our method attains an average DSC of 68.9%\/65.2%\/72.8%, and ASSD of 9.75\/3.82\/1.30 mm on abdomen\/cardiac\/brain images, outperforming the second\u2010best comparison methods by large margins. In the cross\u2010domain task, without one\u2010shot optimization, our method outperforms other deep networks in five out of six cross\u2010domain scenarios and even surpasses symmetric image normalization method (SyN) in two scenarios. By conducting the one\u2010shot optimization, our method successfully surpasses SyN in all six cross\u2010domain\u00a0scenarios.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our method yields favorable results in the single\u2010domain task while ensuring improved generalization and adaptation performance in the cross\u2010domain task, showing its feasibility for the challenging cross\u2010domain registration applications. The code is publicly available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/JuliusWang-7\/EncoderReg\">https:\/\/github.com\/JuliusWang\u20107\/EncoderReg<\/jats:ext-link>.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17565","type":"journal-article","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T08:14:47Z","timestamp":1734423287000},"page":"2305-2315","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Encoding matching criteria for cross\u2010domain deformable image registration"],"prefix":"10.1002","volume":"52","author":[{"given":"Zhuoyuan","family":"Wang","sequence":"first","affiliation":[{"name":"Smart Medical Imaging, Learning and Engineering (SMILE) Lab, Medical UltraSound Image Computing (MUSIC) Lab, School of Biomedical Engineering Shenzhen University Medical School, Shenzhen University Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiqiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Smart Medical Imaging, Learning and Engineering (SMILE) Lab, Medical UltraSound Image Computing (MUSIC) Lab, School of Biomedical Engineering Shenzhen University Medical School, Shenzhen University Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Ni","sequence":"additional","affiliation":[{"name":"Smart Medical Imaging, Learning and Engineering (SMILE) Lab, Medical UltraSound Image Computing (MUSIC) Lab, School of Biomedical Engineering Shenzhen University Medical School, Shenzhen University Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Medical Ultrasound, The First Affiliated Hospital Sun Yat\u2010Sen University Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Wang","sequence":"additional","affiliation":[{"name":"Smart Medical Imaging, Learning and Engineering (SMILE) Lab, Medical UltraSound Image Computing (MUSIC) Lab, School of Biomedical Engineering Shenzhen University Medical School, Shenzhen University Shenzhen China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,12,17]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2013.2265603"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2897538"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102615"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3288136"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102379"},{"key":"e_1_2_9_7_1","doi-asserted-by":"crossref","unstructured":"MaT DaiX ZhangS WenY.PIViT: large deformation image registration with pyramid\u2010iterative vision transformer. 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