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

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...

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Bibliographic Details
Published in:arXiv.org 2024-11
Main Authors: Friederich, Nils, Angelo Jovin Yamachui Sitcheu, Nassal, Annika, Pesch, Matthias, Yildiz, Erenus, Beichter, Maximilian, Scholtes, Lukas, Akbaba, Bahar, Lautenschlager, Thomas, Neumann, Oliver, Dietrich Kohlheyer, Scharr, Hanno, Seiffarth, Johannes, Nöh, Katharina, Mikut, Ralf
Format: Article
Language:English
Subjects:
Automation
Microfluidics
Microscopy
Real time
Online Access:Get full text
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Summary: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.
ISSN:2331-8422