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

2025-03-30

AI Summary

To address event-response latency in high-throughput microfluidic single-cell imaging caused by continuous frame acquisition, this work proposes the first end-to-end event-driven microscopy framework integrating deep learning–based autofocus, real-time segmentation evaluation, and interactive visualization dashboards. We systematically benchmark 11 deep learning segmentation models under single-cell imaging constraints, identifying Cellpose v3 as optimal (Panoptic Quality: 93.58%). We further introduce a lightweight distance-transform-based segmentation method achieving both speed (121 ms inference) and accuracy (PQ: 93.02%). The autofocus module attains a mean absolute error of only 0.0226 μm with inference latency <50 ms. Experimental evaluation reveals that six baseline models fail to meet real-time requirements; our framework demonstrably enhances immediate capture and closed-loop analysis of stochastic biological events.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Hardware-aware ML
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 extmu 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.

Problem

• Enhancing real-time analysis in microfluidic single-cell imaging
• Improving autofocus accuracy and speed for event-driven microscopy
• Evaluating deep learning methods for real-time cell segmentation

Innovation

• Deep Learning autofocusing with low error
• Real-time segmentation using Cellpose3
• Fast distance-based segmentation method