{"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-context-aware-delayed-agglomeration","title":"A Context-aware Delayed Agglomeration Framework for Electron Microscopy Segmentation","arxiv_id":"1406.1476","date":"2014-06-05","proceeding":null,"authors":["Toufiq Parag","Anirban Chakraborty","Stephen Plaza","Lou Scheffer"],"abstract":"Electron Microscopy (EM) image (or volume) segmentation has become\nsignificantly important in recent years as an instrument for connectomics. This\npaper proposes a novel agglomerative framework for EM segmentation. In\nparticular, given an over-segmented image or volume, we propose a novel\nframework for accurately clustering regions of the same neuron. Unlike existing\nagglomerative methods, the proposed context-aware algorithm divides superpixels\n(over-segmented regions) of different biological entities into different\nsubsets and agglomerates them separately. In addition, this paper describes a\n\"delayed\" scheme for agglomerative clustering that postpones some of the merge\ndecisions, pertaining to newly formed bodies, in order to generate a more\nconfident boundary prediction. We report significant improvements attained by\nthe proposed approach in segmentation accuracy over existing standard methods\non 2D and 3D datasets.","url_abs":"http://arxiv.org/abs/1406.1476v5","url_pdf":"http://arxiv.org/pdf/1406.1476v5.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-context-aware-delayed-agglomeration","repo_url":"https://github.com/janelia-flyem/NeuroProof","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"superpixels","task_name":"Superpixels"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}