{"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/mvmm-regnet-a-new-image-registration","title":"MvMM-RegNet: A new image registration framework based on multivariate mixture model and neural network estimation","arxiv_id":"2006.15573","date":"2020-06-28","proceeding":null,"authors":["Xinzhe Luo","Xiahai Zhuang"],"abstract":"Current deep-learning-based registration algorithms often exploit intensity-based similarity measures as the loss function, where dense correspondence between a pair of moving and fixed images is optimized through backpropagation during training. However, intensity-based metrics can be misleading when the assumption of intensity class correspondence is violated, especially in cross-modality or contrast-enhanced images. Moreover, existing learning-based registration methods are predominantly applicable to pairwise registration and are rarely extended to groupwise registration or simultaneous registration with multiple images. In this paper, we propose a new image registration framework based on multivariate mixture model (MvMM) and neural network estimation. A generative model consolidating both appearance and anatomical information is established to derive a novel loss function capable of implementing groupwise registration. We highlight the versatility of the proposed framework for various applications on multimodal cardiac images, including single-atlas-based segmentation (SAS) via pairwise registration and multi-atlas segmentation (MAS) unified by groupwise registration. We evaluated performance on two publicly available datasets, i.e. MM-WHS-2017 and MS-CMRSeg-2019. The results show that the proposed framework achieved an average Dice score of $0.871\\pm 0.025$ for whole-heart segmentation on MR images and $0.783\\pm 0.082$ for myocardium segmentation on LGE MR images.","url_abs":"https://arxiv.org/abs/2006.15573v2","url_pdf":"https://arxiv.org/pdf/2006.15573v2.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":"mvmm-regnet-a-new-image-registration","repo_url":"https://github.com/xzluo97/MvMM-RegNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"heart-segmentation","task_name":"Heart Segmentation"},{"task_slug":"image-registration","task_name":"Image Registration"},{"task_slug":null,"task_name":"Myocardium Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}