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

TL;DR

This paper introduces EAP4EMSIG, an automation pipeline for real-time event-driven microscopy analysis in microfluidic single-cell studies, highlighting initial segmentation results and evaluating state-of-the-art methods.

Contribution

It presents a novel pipeline integrating real-time segmentation for microfluidic microscopy, with initial zero-shot results and comparative analysis of segmentation methods.

Findings

Omnipose achieves highest PQ score of 0.9336

CPN has fastest inference time of 185 ms

Segment Anything is unsuitable for this use case

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.