EAP4EMSIG -- Enhancing Event-Driven Microscopy for Microfluidic Single-Cell Analysis

arXiv (Cornell University)

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.

Authors 15

1. Nils Friederich

   Karlsruhe Institute of Technology

   Affiliation as printed

   Institute for Automation and Applied Informatics (IAI) , Karlsruhe Institute of Technology

   Institute of Biological and Chemical Systems (IBCS) , Karlsruhe Institute of Technology

2. Angelo Jovin Yamachui Sitcheu
3. Annika Nassal

   Karlsruhe Institute of Technology

   Affiliation as printed

   Institute for Automation and Applied Informatics (IAI) , Karlsruhe Institute of Technology

   Institute of Biological and Chemical Systems (IBCS) , Karlsruhe Institute of Technology

4. Erenus Yildiz

   Forschungszentrum Jülich

   Affiliation as printed

   Institute for Data Science and Machine Learning (IAS-8) , Forschungszentrum Jülich GmbH

5. Matthias Pesch

   Forschungszentrum Jülich

   Affiliation as printed

   Institute of Bio-and Geosciences (IBG-1) , Forschungszentrum Jülich GmbH

6. Maximilian Beichter

   Karlsruhe Institute of Technology

   Affiliation as printed

   Institute for Automation and Applied Informatics (IAI) , Karlsruhe Institute of Technology

7. Lukas Scholtes

   Forschungszentrum Jülich

   Affiliation as printed

   Institute for Data Science and Machine Learning (IAS-8) , Forschungszentrum Jülich GmbH

8. Bahar Akbaba

   Karlsruhe Institute of Technology

   Affiliation as printed

   Institute for Automation and Applied Informatics (IAI) , Karlsruhe Institute of Technology

9. Thomas Lautenschlager

   Karlsruhe Institute of Technology

   Affiliation as printed

   Institute for Automation and Applied Informatics (IAI) , Karlsruhe Institute of Technology

10. Oliver Neumann

    Karlsruhe Institute of Technology

    Affiliation as printed

    Institute for Automation and Applied Informatics (IAI) , Karlsruhe Institute of Technology

11. Dietrich Kohlheyer

    Forschungszentrum Jülich

    Affiliation as printed

    Institute of Bio-and Geosciences (IBG-1) , Forschungszentrum Jülich GmbH

12. Hanno Scharr

    Forschungszentrum Jülich

    Affiliation as printed

    Institute for Data Science and Machine Learning (IAS-8) , Forschungszentrum Jülich GmbH

13. Johannes Seiffarth Aachen Computational Systems Biology (AVT.CSB)

    Forschungszentrum Jülich · RWTH Aachen University

    Affiliation as printed

    Computational Systems Biology (AVT-CSB) , RWTH Aachen University

    Institute of Bio-and Geosciences (IBG-1) , Forschungszentrum Jülich GmbH

14. Katharina Nöh

    Forschungszentrum Jülich

    Affiliation as printed

    Institute of Bio-and Geosciences (IBG-1) , Forschungszentrum Jülich GmbH

15. Ralf Mikut

    Karlsruhe Institute of Technology

    Affiliation as printed

    Institute for Automation and Applied Informatics (IAI) , Karlsruhe Institute of Technology