让微流控单细胞成像实时响应随机事件，提升高通量实验效率
EAP4EMSIG -- Enhancing Event-Driven Microscopy for Microfluidic Single-Cell Analysis
- 用MLP模型快速预测对焦偏移，87毫秒内完成
- 细胞分割最高达93.36%准确率，最快121毫秒
- 集成自动对焦、实时分割与分析仪表盘，适合单细胞研究者
微流控活细胞成像（MLCI）可获取微生物细胞工厂的数据，但连续采集受限于缺乏实时洞察，难以及时响应随机事件。我们提出实验自动化管道EAP4EMSIG，包含三项核心组件：基于多层感知机（MLP）的快速高精度自动对焦方法，实时分割方法评估，以及实时数据分析仪表盘。该MLP对焦方法实现0.105 μm的平均绝对误差，推理时间仅87毫秒。在11种深度学习分割方法中，Cellpose达到93.36%的全景质量（PQ），而基于距离的方法最快（121毫秒，PQ 93.02%）。
原文摘要 · Abstract (English)
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 $μ$m 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 %).
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