EAP4EMSIG - Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis
Nils Friederich,
Angelo Jovin Yamachui Sitcheu,
Annik Nassal,
Matthias Pesch,
Erenus Yildiz,
Maximilian Beichter,
Lulkas Scholtes,
Bahar Akbaba,
Thomas Lautenschlager,
Oliver Neumann,
Dietrich Kohlheyer,
Hanno Scharr,
Johannes Seiffarth,
Katharina Nöh,
Ralf Mikut
Kapitel/Beitrag aus dem Buch: Schulte, H et al. 2024. Proceedings - 34. Workshop Computational Intelligence: Berlin, 21.-22. November 2024.
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.
