{"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/fast-simple-calcium-imaging-segmentation-with","title":"Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks","arxiv_id":"1707.06314","date":"2017-07-19","proceeding":null,"authors":["Aleksander Klibisz","Derek Rose","Matthew Eicholtz","Jay Blundon","Stanislav Zakharenko"],"abstract":"Calcium imaging is a technique for observing neuron activity as a series of\nimages showing indicator fluorescence over time. Manually segmenting neurons is\ntime-consuming, leading to research on automated calcium imaging segmentation\n(ACIS). We evaluated several deep learning models for ACIS on the Neurofinder\ncompetition datasets and report our best model: U-Net2DS, a fully convolutional\nnetwork that operates on 2D mean summary images. U-Net2DS requires minimal\ndomain-specific pre/post-processing and parameter adjustment, and predictions\nare made on full $512\\times512$ images at $\\approx$9K images per minute. It\nranks third in the Neurofinder competition ($F_1=0.569$) and is the best model\nto exclusively use deep learning. We also demonstrate useful segmentations on\ndata from outside the competition. The model's simplicity, speed, and quality\nresults make it a practical choice for ACIS and a strong baseline for more\ncomplex models in the future.","url_abs":"http://arxiv.org/abs/1707.06314v1","url_pdf":"http://arxiv.org/pdf/1707.06314v1.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":"fast-simple-calcium-imaging-segmentation-with","repo_url":"https://github.com/alexklibisz/deep-calcium","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"fast-simple-calcium-imaging-segmentation-with","repo_url":"https://github.com/ankit-vaghela30/Cilia-Segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"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}