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Webinar: AI-Driven, Label-Free Quantification of Cell Viability
Drug discovery is progressing to the use of more relevant and precious cell types to improve translational relevance of in vitro assays. These cell types often have increased sensitivity to external perturbance, including the presence of fluorescent reagents and reporter dyes.
Therefore, there is a vital need to improve abilities to analyze complex images of cells without reliance on fluorescence readouts. The use of artificial intelligence (AI) for image analysis has expanded hugely in recent years, and here we introduce a new software module that applies this technology to live-cell imaging and analysis.
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Dr. Gillian LovellSenior Scientist, BioAnalyticsSartorius
Gillian is a Senior Scientist at Sartorius. Within the BioAnalytics group, she has worked across multiple research areas developing live-cell imaging assays and analytical methods, building these into customer-facing applications.
Gillian obtained her PhD in Chemical Biology from Imperial College London using multidisciplinary techniques to synthesize probes for biological targets and evaluate their effects with biochemical and cell-based assays. After a period of postdoctoral research, she joined Sartorius in 2015 to work on the Incucyte platform.
Dr. Daniel Akashi PortoImage Processing Engineer, Advanced AlgorithmsSartorius
Daniel is an Image Processing Engineer at Sartorius. Since joining in 2019, he has worked with the Advanced Algorithms group, focusing on development of analysis modules for the Incucyte platform.
Daniel received his PhD in BioEngineering from the Georgia Institute of Technology. His research involved applying computer vision to microscopy instrumentation and data analysis, with the goal of improving experimental capabilities in the study of neuronal activity and behavior in Caenorhabditis elegans.
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