{"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/livecell-a-large-scale-dataset-for-label-free","title":"LIVECell—A large-scale dataset for label-free live cell segmentation","arxiv_id":null,"date":"2021-08-30","proceeding":"Nature Methods 2021 8","authors":["Christoffer Edlund","Timothy R. Jackson","Nabeel Khalid","Nicola Bevan","Timothy Dale","Andreas Dengel","Sheraz Ahmed","Johan Trygg","Rickard Sjögren"],"abstract":"Light microscopy combined with well-established protocols of two-dimensional cell culture facilitates high-throughput quantitative imaging to study biological phenomena. Accurate segmentation of individual cells in images enables exploration of complex biological questions, but can require sophisticated imaging processing pipelines in cases of low contrast and high object density. Deep learning-based methods are considered state-of-the-art for image segmentation but typically require vast amounts of annotated data, for which there is no suitable resource available in the field of label-free cellular imaging. Here, we present LIVECell, a large, high-quality, manually annotated and expert-validated dataset of phase-contrast images, consisting of over 1.6 million cells from a diverse set of cell morphologies and culture densities. To further demonstrate its use, we train convolutional neural network-based models using LIVECell and evaluate model segmentation accuracy with a proposed a suite of benchmarks.","url_abs":"https://www.nature.com/articles/s41592-021-01249-6","url_pdf":"https://www.nature.com/articles/s41592-021-01249-6.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":"livecell-a-large-scale-dataset-for-label-free","repo_url":"https://github.com/sartorius-research/LIVECell","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"cell-segmentation","task_name":"Cell Segmentation"},{"task_slug":"culture","task_name":"Cultural Vocal Bursts Intensity Prediction"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"livecell","name":"LIVECell","full_name":"Label-free In Vitro image Examples of Cells"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/cell-segmentation-on-livecell","task":"Cell Segmentation","dataset":"LIVECell","model":"Cascade Mask RCNN-ResNest-200","rank_in_archive_order":1,"of":3,"metrics":{"LIVECell Extrapolation (A172)":"1328","LIVECell Extrapolation (A549)":"1403","LIVECell Transferability":"0.98","mask AFNR":"45.3","mask AP":"47.9"},"uses_additional_data":false},{"leaderboard":"/sota/cell-segmentation-on-livecell","task":"Cell Segmentation","dataset":"LIVECell","model":"CenterMask-VoVNet2-FPN","rank_in_archive_order":2,"of":3,"metrics":{"LIVECell Extrapolation (A172)":"1948","LIVECell Extrapolation (A549)":"2031","LIVECell Transferability":"1.21","mask AFNR":"52.2","mask AP":"47.8"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}