Microfluidic Live-Cell Imaging (MLCI) yields data on microbial cell factories. However, continuous acquisition is challenging as high-throughput experiments often lack real-time 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 (EAP4EMSIG): a fast, accurate Multi-Layer Perceptron (MLP)-based autofocusing method predicting the focus offset, an evaluation of real-time segmentation methods and a real-time data analysis dashboard. Our MLP-based autofocusing achieves a Mean Absolute Error (MAE) of 0.105 μm with inference times from 87 ms. Among eleven evaluated Deep Learning (DL) segmentation methods, Cellpose reached a Panoptic Quality (PQ) of 93.36 %, while a distance-based method was fastest (121 ms, Panoptic Quality 93.02 %).
@article{arxiv.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}
}