{"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/joint-learning-of-motion-estimation-and","title":"Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences","arxiv_id":"1806.04066","date":"2018-06-11","proceeding":null,"authors":["Chen Qin","Wenjia Bai","Jo Schlemper","Steffen E. Petersen","Stefan K. Piechnik","Stefan Neubauer","Daniel Rueckert"],"abstract":"Cardiac motion estimation and segmentation play important roles in\nquantitatively assessing cardiac function and diagnosing cardiovascular\ndiseases. In this paper, we propose a novel deep learning method for joint\nestimation of motion and segmentation from cardiac MR image sequences. The\nproposed network consists of two branches: a cardiac motion estimation branch\nwhich is built on a novel unsupervised Siamese style recurrent spatial\ntransformer network, and a cardiac segmentation branch that is based on a fully\nconvolutional network. In particular, a joint multi-scale feature encoder is\nlearned by optimizing the segmentation branch and the motion estimation branch\nsimultaneously. This enables the weakly-supervised segmentation by taking\nadvantage of features that are unsupervisedly learned in the motion estimation\nbranch from a large amount of unannotated data. Experimental results using\ncardiac MRI images from 220 subjects show that the joint learning of both tasks\nis complementary and the proposed models outperform the competing methods\nsignificantly in terms of accuracy and speed.","url_abs":"http://arxiv.org/abs/1806.04066v1","url_pdf":"http://arxiv.org/pdf/1806.04066v1.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":"joint-learning-of-motion-estimation-and","repo_url":"https://github.com/cq615/Joint-Motion-Estimation-and-Segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"cardiac-segmentation","task_name":"Cardiac Segmentation"},{"task_slug":"motion-estimation","task_name":"Motion Estimation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"weakly-supervised-segmentation","task_name":"Weakly supervised segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1806.04066","atlas_url":"https://app.syntology.ai/?focus=1806.04066","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}