自动化显微镜分析系统实现单细胞实时事件识别
EAP4EMSIG -- Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis
- 构建实验自动化流水线，实现实时细胞分割与事件检测
- Omnipose模型达0.9336的全景质量分，最快185毫秒推理
- 适合生物实验自动化与高通量单细胞分析研究者
微流控活细胞成像（MLCI）能生成高质量数据，用于精细研究细胞生长动态。然而，长时间连续获取数据并实现实时事件分类仍具挑战，尤其在成像与随机生物学交汇处。为此，我们提出面向事件驱动显微镜的智能微流控单细胞分析实验自动化流水线（EAP4EMSIG）。初步零样本结果显示，评估的四种前沿分割方法中，Omnipose取得最高全景质量（PQ）得分0.9336；而轮廓提案网络（CPN）以185毫秒的最短推理时间及0.8575的次高PQ表现胜出。此外，视觉基础模型Segment Anything在此场景下不适用。
原文摘要 · Abstract (English)
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.
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