EAP4EMSIG - Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis

Abstract

Microfluidic Live-Cell Imaging (MLCI) generates high-quality data that allows biotechnologists to study cellular growth dynamics in detail. However, obtaining these continuous data over extended periods is challenging, particularly in achieving accurate and consistent real-time event classification at the intersection of imaging and stochastic biology. To address this issue, we introduce the Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis (EAP4EMSIG). In particular, we present initial zero-shot results from the real-time segmentation module of our approach. Our findings indicate that among four State-Of-The- Art (SOTA) segmentation methods evaluated, Omnipose delivers the highest Panoptic Quality (PQ) score of 0.9336, while Contour Proposal Network (CPN) achieves the fastest inference time of 185 ms with the second-highest PQ score of 0.8575. Furthermore, we observed that the vision foundation model Segment Anything is unsuitable for this particular use case.

Authors 15

1. Nils Friederich

   Karlsruhe Institute of Technology

   Affiliation as printed

   Institute for Automation and Applied Informatics (IAI)

   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)

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

4. Matthias Pesch
   Affiliation as printed

   Institute of Bio-and Geosciences (IBG-

5. Erenus Yildiz
   Affiliation as printed

   Institute for Data Science and Machine Learning (IAS-

6. Maximilian Beichter
   Affiliation as printed

   Institute for Automation and Applied Informatics (IAI)

7. Lukas Scholtes
   Affiliation as printed

   Institute for Data Science and Machine Learning (IAS-

8. Bahar Akbaba
   Affiliation as printed

   Institute for Automation and Applied Informatics (IAI)

9. Thomas Lautenschlager
   Affiliation as printed

   Institute for Automation and Applied Informatics (IAI)

10. Oliver Neumann
    Affiliation as printed

    Institute for Automation and Applied Informatics (IAI)

11. Dietrich Kohlheyer
    Affiliation as printed

    Institute of Bio-and Geosciences (IBG-

12. Hanno Scharr
    Affiliation as printed

    Institute for Data Science and Machine Learning (IAS-

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

    RWTH Aachen University

    Affiliation as printed

    Computational Systems Biology (AVT-CSB ) RWTH Aachen University

    Institute of Bio-and Geosciences (IBG-

14. Katharina Nöh
    Affiliation as printed

    Institute of Bio-and Geosciences (IBG-

15. Ralf Mikut
    Affiliation as printed

    Institute for Automation and Applied Informatics (IAI)