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

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.

@article{friederich2025_2504.00047,
  title={ EAP4EMSIG -- Enhancing Event-Driven Microscopy for Microfluidic Single-Cell Analysis },
  author={ Nils Friederich and Angelo Jovin Yamachui Sitcheu and Annika Nassal and Erenus Yildiz and Matthias Pesch and Maximilian Beichter and Lukas Scholtes and Bahar Akbaba and Thomas Lautenschlager and Oliver Neumann and Dietrich Kohlheyer and Hanno Scharr and Johannes Seiffarth and Katharina Nöh and Ralf Mikut },
  journal={arXiv preprint arXiv:2504.00047},
  year={ 2025 }
}