{"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/eap4emsig-enhancing-event-driven-microscopy","title":"EAP4EMSIG -- Enhancing Event-Driven Microscopy for Microfluidic Single-Cell Analysis","arxiv_id":"2504.00047","date":"2025-03-30","proceeding":null,"authors":["Nils Friederich","Angelo Jovin Yamachui Sitcheu","Annika Nassal","Erenus Yildiz","Matthias Pesch","Maximilian Beichter","Lukas Scholtes","Bahar Akbaba","Thomas Lautenschlager","Oliver Neumann","Dietrich Kohlheyer","Hanno Scharr","Johannes Seiffarth","Katharina Nöh","Ralf Mikut"],"abstract":"Microfluidic Live-Cell Imaging yields data on microbial cell factories. However, continuous acquisition is challenging as high-throughput experiments often lack realtime insights, delaying responses to stochastic events. We introduce three components in the Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cell Analysis: a fast, accurate Deep Learning autofocusing method predicting the focus offset, an evaluation of real-time segmentation methods and a realtime data analysis dashboard. Our autofocusing achieves a Mean Absolute Error of 0.0226\\textmu m with inference times below 50~ms. Among eleven Deep Learning segmentation methods, Cellpose~3 reached a Panoptic Quality of 93.58\\%, while a distance-based method is fastest (121~ms, Panoptic Quality 93.02\\%). All six Deep Learning Foundation Models were unsuitable for real-time segmentation.","url_abs":"https://arxiv.org/abs/2504.00047v1","url_pdf":"https://arxiv.org/pdf/2504.00047v1.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":"eap4emsig-enhancing-event-driven-microscopy","repo_url":"https://github.com/NVIDIA/TensorRT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}