{"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/segment-anything-for-microscopy","title":"Segment Anything for Microscopy","arxiv_id":null,"date":"2023-08-22","proceeding":"bioRxiv 2023 8","authors":["Anwai Archit","Sushmita Nair","Nabeel Khalid","Paul Hilt","Vikas Rajashekar","Marei Freitag","Sagnik Gupta","Andreas Dengel","Sheraz Ahmed","Constantin Pape"],"abstract":"We present Segment Anything for Microscopy, a tool for interactive and automatic segmentation and tracking of objects in multi-dimensional microscopy data. Our method is based on Segment Anything, a vision foundation model for image segmentation. We extend it by training specialized models for microscopy data that significantly improve segmentation quality for a wide range of imaging conditions. We also implement annotation tools for interactive (volumetric) segmentation and tracking, that speed up data annotation significantly compared to established tools. Our work constitutes the first application of vision foundation models to microscopy, laying the groundwork for solving image analysis problems in these domains with a small set of powerful deep learning architectures.","url_abs":"https://www.biorxiv.org/content/10.1101/2023.08.21.554208v1","url_pdf":"https://www.biorxiv.org/content/10.1101/2023.08.21.554208v1.full.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":"segment-anything-for-microscopy","repo_url":"https://github.com/computational-cell-analytics/micro-sam","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"object-tracking","task_name":"Object Tracking"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}