{"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/recurrent-image-registration-using-mutual","title":"Recursive Deformable Image Registration Network with Mutual Attention","arxiv_id":"2206.01863","date":"2022-06-04","proceeding":null,"authors":["Jian-Qing Zheng","Ziyang Wang","Baoru Huang","Ngee Han Lim","Tonia Vincent","Bartlomiej W. Papiez"],"abstract":"Deformable image registration, estimating the spatial transformation between different images, is an important task in medical imaging. Many previous studies have used learning-based methods for multi-stage registration to perform 3D image registration to improve performance. The performance of the multi-stage approach, however, is limited by the size of the receptive field where complex motion does not occur at a single spatial scale. We propose a new registration network combining recursive network architecture and mutual attention mechanism to overcome these limitations. Compared with the state-of-the-art deep learning methods, our network based on the recursive structure achieves the highest accuracy in lung Computed Tomography (CT) data set (Dice score of 92\\% and average surface distance of 3.8mm for lungs) and one of the most accurate results in abdominal CT data set with 9 organs of various sizes (Dice score of 55\\% and average surface distance of 7.8mm). We also showed that adding 3 recursive networks is sufficient to achieve the state-of-the-art results without a significant increase in the inference time.","url_abs":"https://arxiv.org/abs/2206.01863v2","url_pdf":"https://arxiv.org/pdf/2206.01863v2.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":[],"tasks":[{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"image-registration","task_name":"Image Registration"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-registration-on-unpaired-abdomen-ct","task":"Image Registration","dataset":"Unpaired-abdomen-CT","model":"RMAn","rank_in_archive_order":6,"of":9,"metrics":{"ASD":"7.78","DSC":"0.55"},"uses_additional_data":false},{"leaderboard":"/sota/image-registration-on-unpaired-abdomen-ct","task":"Image Registration","dataset":"Unpaired-abdomen-CT","model":"Dnet","rank_in_archive_order":9,"of":9,"metrics":{"ASD":"8.72","DSC":"0.47"},"uses_additional_data":false},{"leaderboard":"/sota/image-registration-on-unpaired-lung-ct","task":"Image Registration","dataset":"Unpaired-lung-CT","model":"RMAn","rank_in_archive_order":3,"of":4,"metrics":{"ASD":"3.83","DSC":"0.92"},"uses_additional_data":false},{"leaderboard":"/sota/image-registration-on-unpaired-lung-ct","task":"Image Registration","dataset":"Unpaired-lung-CT","model":"Dnet","rank_in_archive_order":4,"of":4,"metrics":{"ASD":"5.01","DSC":"0.88"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}