{"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-convolutional-autoencoder-approach-to-learn","title":"A Convolutional Autoencoder Approach to Learn Volumetric Shape Representations for Brain Structures","arxiv_id":"1810.07746","date":"2018-10-17","proceeding":null,"authors":["Evan M. Yu","Mert R. Sabuncu"],"abstract":"We propose a novel machine learning strategy for studying neuroanatomical\nshape variation. Our model works with volumetric binary segmentation images,\nand requires no pre-processing such as the extraction of surface points or a\nmesh. The learned shape descriptor is invariant to affine transformations,\nincluding shifts, rotations and scaling. Thanks to the adopted autoencoder\nframework, inter-subject differences are automatically enhanced in the learned\nrepresentation, while intra-subject variances are minimized. Our experimental\nresults on a shape retrieval task showed that the proposed representation\noutperforms a state-of-the-art benchmark for brain structures extracted from\nMRI scans.","url_abs":"http://arxiv.org/abs/1810.07746v1","url_pdf":"http://arxiv.org/pdf/1810.07746v1.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-convolutional-autoencoder-approach-to-learn","repo_url":"https://github.com/evanmy/voxel_shape_analysis","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"retrieval","task_name":"Retrieval"}],"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}