{"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/a-cross-center-smoothness-prior-for","title":"A cross-center smoothness prior for variational Bayesian brain tissue segmentation","arxiv_id":"1903.04191","date":"2019-03-11","proceeding":null,"authors":["Wouter M. Kouw","Silas N. Ørting","Jens Petersen","Kim S. Pedersen","Marleen de Bruijne"],"abstract":"Suppose one is faced with the challenge of tissue segmentation in MR images,\nwithout annotators at their center to provide labeled training data. One option\nis to go to another medical center for a trained classifier. Sadly, tissue\nclassifiers do not generalize well across centers due to voxel intensity shifts\ncaused by center-specific acquisition protocols. However, certain aspects of\nsegmentations, such as spatial smoothness, remain relatively consistent and can\nbe learned separately. Here we present a smoothness prior that is fit to\nsegmentations produced at another medical center. This informative prior is\npresented to an unsupervised Bayesian model. The model clusters the voxel\nintensities, such that it produces segmentations that are similarly smooth to\nthose of the other medical center. In addition, the unsupervised Bayesian model\nis extended to a semi-supervised variant, which needs no visual interpretation\nof clusters into tissues.","url_abs":"http://arxiv.org/abs/1903.04191v1","url_pdf":"http://arxiv.org/pdf/1903.04191v1.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":"a-cross-center-smoothness-prior-for","repo_url":"https://github.com/wmkouw/cc-smoothprior","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}