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001037178 0247_ $$2doi$$a10.48550/ARXIV.2411.05030
001037178 037__ $$aFZJ-2025-00523
001037178 1001_ $$0P:(DE-HGF)0$$aFriederich, Nils$$b0
001037178 245__ $$aEAP4EMSIG -- Experiment Automation Pipeline for Event-Driven Microscopy to Smart Microfluidic Single-Cells Analysis
001037178 260__ $$barXiv$$c2024
001037178 3367_ $$0PUB:(DE-HGF)25$$2PUB:(DE-HGF)$$aPreprint$$bpreprint$$mpreprint$$s1736837909_18518
001037178 3367_ $$2ORCID$$aWORKING_PAPER
001037178 3367_ $$028$$2EndNote$$aElectronic Article
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001037178 3367_ $$2BibTeX$$aARTICLE
001037178 3367_ $$2DataCite$$aOutput Types/Working Paper
001037178 500__ $$aarXiv, arXiv:2411.05030 [q-bio.QM]
001037178 520__ $$aMicrofluidic 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.
001037178 536__ $$0G:(DE-HGF)POF4-2171$$a2171 - Biological and environmental resources for sustainable use (POF4-217)$$cPOF4-217$$fPOF IV$$x0
001037178 588__ $$aDataset connected to DataCite
001037178 650_7 $$2Other$$aQuantitative Methods (q-bio.QM)
001037178 650_7 $$2Other$$aComputer Vision and Pattern Recognition (cs.CV)
001037178 650_7 $$2Other$$aImage and Video Processing (eess.IV)
001037178 650_7 $$2Other$$aFOS: Biological sciences
001037178 650_7 $$2Other$$aFOS: Computer and information sciences
001037178 650_7 $$2Other$$aFOS: Electrical engineering, electronic engineering, information engineering
001037178 7001_ $$0P:(DE-HGF)0$$aYamachui Sitcheu, A. J.$$b1
001037178 7001_ $$0P:(DE-HGF)0$$aNassal, Annika$$b2
001037178 7001_ $$0P:(DE-Juel1)199011$$aPesch, Matthias$$b3$$ufzj
001037178 7001_ $$aYildiz, Erenus$$b4
001037178 7001_ $$aBeichter, Maximilian$$b5
001037178 7001_ $$0P:(DE-Juel1)173690$$aScholtes, Lukas$$b6$$ufzj
001037178 7001_ $$0P:(DE-HGF)0$$aAkbaba, Bahar$$b7
001037178 7001_ $$0P:(DE-HGF)0$$aLautenschlager, Thomas$$b8
001037178 7001_ $$aNeumann, Oliver$$b9
001037178 7001_ $$0P:(DE-Juel1)140195$$aKohlheyer, Dietrich$$b10$$ufzj
001037178 7001_ $$0P:(DE-Juel1)129394$$aScharr, Hanno$$b11$$ufzj
001037178 7001_ $$aSeiffarth, Johannes$$b12
001037178 7001_ $$0P:(DE-Juel1)129051$$aNöh, Katharina$$b13$$ufzj
001037178 7001_ $$0P:(DE-HGF)0$$aMikut, Ralf$$b14
001037178 773__ $$a10.48550/ARXIV.2411.05030
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001037178 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)199011$$aForschungszentrum Jülich$$b3$$kFZJ
001037178 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)173690$$aForschungszentrum Jülich$$b6$$kFZJ
001037178 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)140195$$aForschungszentrum Jülich$$b10$$kFZJ
001037178 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129394$$aForschungszentrum Jülich$$b11$$kFZJ
001037178 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129051$$aForschungszentrum Jülich$$b13$$kFZJ
001037178 9131_ $$0G:(DE-HGF)POF4-217$$1G:(DE-HGF)POF4-210$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-2171$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lErde im Wandel – Unsere Zukunft nachhaltig gestalten$$vFür eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten$$x0
001037178 9141_ $$y2024
001037178 9201_ $$0I:(DE-Juel1)IBG-1-20101118$$kIBG-1$$lBiotechnologie$$x0
001037178 9201_ $$0I:(DE-Juel1)IAS-8-20210421$$kIAS-8$$lDatenanalyse und Maschinenlernen$$x1
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001037178 980__ $$aUNRESTRICTED