EAP4EMSIG -- Enhancing Event-Driven Microscopy for Microfluidic Single-Cell Analysis
Journal:
arXiv
Published Date:
Mar 30, 2025
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.
