{"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/an-unsupervised-learning-model-for-deformable","title":"An Unsupervised Learning Model for Deformable Medical Image Registration","arxiv_id":"1802.02604","date":"2018-02-07","proceeding":"CVPR 2018 6","authors":["Guha Balakrishnan","Amy Zhao","Mert R. Sabuncu","John Guttag","Adrian V. Dalca"],"abstract":"We present a fast learning-based algorithm for deformable, pairwise 3D\nmedical image registration. Current registration methods optimize an objective\nfunction independently for each pair of images, which can be time-consuming for\nlarge data. We define registration as a parametric function, and optimize its\nparameters given a set of images from a collection of interest. Given a new\npair of scans, we can quickly compute a registration field by directly\nevaluating the function using the learned parameters. We model this function\nusing a convolutional neural network (CNN), and use a spatial transform layer\nto reconstruct one image from another while imposing smoothness constraints on\nthe registration field. The proposed method does not require supervised\ninformation such as ground truth registration fields or anatomical landmarks.\nWe demonstrate registration accuracy comparable to state-of-the-art 3D image\nregistration, while operating orders of magnitude faster in practice. Our\nmethod promises to significantly speed up medical image analysis and processing\npipelines, while facilitating novel directions in learning-based registration\nand its applications. Our code is available at\nhttps://github.com/balakg/voxelmorph .","url_abs":"http://arxiv.org/abs/1802.02604v3","url_pdf":"http://arxiv.org/pdf/1802.02604v3.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":"an-unsupervised-learning-model-for-deformable","repo_url":"https://github.com/balakg/voxelmorph","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"an-unsupervised-learning-model-for-deformable","repo_url":"https://github.com/voxelmorph/voxelmorph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"an-unsupervised-learning-model-for-deformable","repo_url":"https://github.com/yh854/Rigid-Registration-of-3D-MRI-Based-on-Unsupervised-Learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"deformable-medical-image-registration","task_name":"Deformable Medical Image Registration"},{"task_slug":"image-registration","task_name":"Image Registration"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"},{"task_slug":"medical-image-registration","task_name":"Medical Image Registration"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.02604","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}