EAP4EMSIG -- Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis
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| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915199390318592 |
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| author | Friederich, Nils Sitcheu, Angelo Jovin Yamachui Nassal, Annika Pesch, Matthias Yildiz, Erenus Beichter, Maximilian Scholtes, Lukas Akbaba, Bahar Lautenschlager, Thomas Neumann, Oliver Kohlheyer, Dietrich Scharr, Hanno Seiffarth, Johannes Nöh, Katharina Mikut, Ralf |
| author_facet | Friederich, Nils Sitcheu, Angelo Jovin Yamachui Nassal, Annika Pesch, Matthias Yildiz, Erenus Beichter, Maximilian Scholtes, Lukas Akbaba, Bahar Lautenschlager, Thomas Neumann, Oliver Kohlheyer, Dietrich Scharr, Hanno Seiffarth, Johannes Nöh, Katharina Mikut, Ralf |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_05030 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | EAP4EMSIG -- Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis Friederich, Nils Sitcheu, Angelo Jovin Yamachui Nassal, Annika Pesch, Matthias Yildiz, Erenus Beichter, Maximilian Scholtes, Lukas Akbaba, Bahar Lautenschlager, Thomas Neumann, Oliver Kohlheyer, Dietrich Scharr, Hanno Seiffarth, Johannes Nöh, Katharina Mikut, Ralf Quantitative Methods Computer Vision and Pattern Recognition Image and Video Processing 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. |
| title | EAP4EMSIG -- Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis |
| topic | Quantitative Methods Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2411.05030 |