{"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/search-for-temporal-cell-segmentation","title":"Search for temporal cell segmentation robustness in phase-contrast microscopy videos","arxiv_id":"2112.08817","date":"2021-12-16","proceeding":null,"authors":["Estibaliz Gómez-de-Mariscal","Hasini Jayatilaka","Özgün Çiçek","Thomas Brox","Denis Wirtz","Arrate Muñoz-Barrutia"],"abstract":"Studying cell morphology changes in time is critical to understanding cell migration mechanisms. In this work, we present a deep learning-based workflow to segment cancer cells embedded in 3D collagen matrices and imaged with phase-contrast microscopy. Our approach uses transfer learning and recurrent convolutional long-short term memory units to exploit the temporal information from the past and provide a consistent segmentation result. Lastly, we propose a geometrical-characterization approach to studying cancer cell morphology. Our approach provides stable results in time, and it is robust to the different weight initialization or training data sampling. We introduce a new annotated dataset for 2D cell segmentation and tracking, and an open-source implementation to replicate the experiments or adapt them to new image processing problems.","url_abs":"https://arxiv.org/abs/2112.08817v1","url_pdf":"https://arxiv.org/pdf/2112.08817v1.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":"search-for-temporal-cell-segmentation","repo_url":"https://github.com/esgomezm/microscopy-dl-suite-tf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"cell-segmentation","task_name":"Cell Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[{"slug":"ht1080wt-cells-3d-collagen-type-i-matrices","name":"HT1080WT cells - 3D collagen type I matrices","full_name":"HT1080WT cells embedded in 3D collagen type I matrices - manual annotations for cell instance segmentation and tracking"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2112.08817","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}