{"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/focused-proofreading-efficiently-extracting","title":"Focused Proofreading: Efficiently Extracting Connectomes from Segmented EM Images","arxiv_id":"1409.1199","date":"2014-09-03","proceeding":null,"authors":["Stephen M. Plaza"],"abstract":"Identifying complex neural circuitry from electron microscopic (EM) images\nmay help unlock the mysteries of the brain. However, identifying this circuitry\nrequires time-consuming, manual tracing (proofreading) due to the size and\nintricacy of these image datasets, thus limiting state-of-the-art analysis to\nvery small brain regions. Potential avenues to improve scalability include\nautomatic image segmentation and crowd sourcing, but current efforts have had\nlimited success. In this paper, we propose a new strategy, focused\nproofreading, that works with automatic segmentation and aims to limit\nproofreading to the regions of a dataset that are most impactful to the\nresulting circuit. We then introduce a novel workflow, which exploits\nbiological information such as synapses, and apply it to a large dataset in the\nfly optic lobe. With our techniques, we achieve significant tracing speedups of\n3-5x without sacrificing the quality of the resulting circuit. Furthermore, our\nmethodology makes the task of proofreading much more accessible and hence\npotentially enhances the effectiveness of crowd sourcing.","url_abs":"http://arxiv.org/abs/1409.1199v1","url_pdf":"http://arxiv.org/pdf/1409.1199v1.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":"focused-proofreading-efficiently-extracting","repo_url":"https://github.com/janelia-flyem/NeuroProof","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"art-analysis","task_name":"Art Analysis"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}