{"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/deformably-registering-and-annotating-whole","title":"Deformably Registering and Annotating Whole CLARITY Brains to an Atlas via Masked LDDMM","arxiv_id":"1605.02060","date":"2016-05-06","proceeding":null,"authors":["Kwame S. Kutten","Joshua T. Vogelstein","Nicolas Charon","Li Ye","Karl Deisseroth","Michael I. Miller"],"abstract":"The CLARITY method renders brains optically transparent to enable\nhigh-resolution imaging in the structurally intact brain. Anatomically\nannotating CLARITY brains is necessary for discovering which regions contain\nsignals of interest. Manually annotating whole-brain, terabyte CLARITY images\nis difficult, time-consuming, subjective, and error-prone. Automatically\nregistering CLARITY images to a pre-annotated brain atlas offers a solution,\nbut is difficult for several reasons. Removal of the brain from the skull and\nsubsequent storage and processing cause variable non-rigid deformations, thus\ncompounding inter-subject anatomical variability. Additionally, the signal in\nCLARITY images arises from various biochemical contrast agents which only\nsparsely label brain structures. This sparse labeling challenges the most\ncommonly used registration algorithms that need to match image histogram\nstatistics to the more densely labeled histological brain atlases. The standard\nmethod is a multiscale Mutual Information B-spline algorithm that dynamically\ngenerates an average template as an intermediate registration target. We\ndetermined that this method performs poorly when registering CLARITY brains to\nthe Allen Institute's Mouse Reference Atlas (ARA), because the image histogram\nstatistics are poorly matched. Therefore, we developed a method (Mask-LDDMM)\nfor registering CLARITY images, that automatically find the brain boundary and\nlearns the optimal deformation between the brain and atlas masks. Using\nMask-LDDMM without an average template provided better results than the\nstandard approach when registering CLARITY brains to the ARA. The LDDMM\npipelines developed here provide a fast automated way to anatomically annotate\nCLARITY images. Our code is available as open source software at\nhttp://NeuroData.io.","url_abs":"http://arxiv.org/abs/1605.02060v1","url_pdf":"http://arxiv.org/pdf/1605.02060v1.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":"deformably-registering-and-annotating-whole","repo_url":"https://github.com/neurodata/ndreg","is_official":1,"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}