{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/recursive-refinement-network-for-deformable","title":"Recursive Refinement Network for Deformable Lung Registration between Exhale and Inhale CT Scans","arxiv_id":"2106.07608","date":"2021-06-14","proceeding":null,"authors":["Xinzi He","Jia Guo","Xuzhe Zhang","Hanwen Bi","Sarah Gerard","David Kaczka","Amin Motahari","Eric Hoffman","Joseph Reinhardt","R. Graham Barr","Elsa Angelini","Andrew Laine"],"abstract":"Unsupervised learning-based medical image registration approaches have witnessed rapid development in recent years. We propose to revisit a commonly ignored while simple and well-established principle: recursive refinement of deformation vector fields across scales. We introduce a recursive refinement network (RRN) for unsupervised medical image registration, to extract multi-scale features, construct normalized local cost correlation volume and recursively refine volumetric deformation vector fields. RRN achieves state of the art performance for 3D registration of expiratory-inspiratory pairs of CT lung scans. On DirLab COPDGene dataset, RRN returns an average Target Registration Error (TRE) of 0.83 mm, which corresponds to a 13% error reduction from the best result presented in the leaderboard. In addition to comparison with conventional methods, RRN leads to 89% error reduction compared to deep-learning-based peer approaches.","url_abs":"https://arxiv.org/abs/2106.07608v1","url_pdf":"https://arxiv.org/pdf/2106.07608v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"recursive-refinement-network-for-deformable","repo_url":"https://github.com/Novestars/Recursive_Refinement_Network","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-registration","task_name":"Image Registration"},{"task_slug":"medical-image-registration","task_name":"Medical Image Registration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-registration-on-dir-lab-copdgene","task":"Image Registration","dataset":"DIR-LAB COPDgene","model":"Recursive Refinement Network","rank_in_archive_order":1,"of":1,"metrics":{"landmarks":"0.83"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2106.07608","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}