Learn how modern deep learning-based approaches have the potential to reduce or replace fluorescence markers in live cell imaging. Use case included. more
Benefits of Deep Learning and AI in Microscopy: Label-Free Nucleus Detection in Micro Well Plates
New AI approaches have potential to reduce or replace fluorescence markers in live cell imagingDr. Mike Woerdemann, Olympus
Label-free observation of biological samples has recently seen a significant increase in importance with the dramatic improvements in image analysis by machine learning methods. Deep learning-based approaches can provide new access to information encoded in transmitted light images:
- They have the potential to replace fluorescence markers used for structural staining of cells or compartments.
- They give clear advantages in solving challenges of transmitted light analysis like low contrast, dust, imperfections, particles, condensation artefacts etc.
Use of Olympus’ AI-based approach brings significant benefits to many live-cell analysis workflows. Aside from the improved accuracy, using bright field images also avoids the need for using genetic modifications or nucleus markers. Not only does this save time on sample preparation, it also saves the fluorescence channel for other markers. Furthermore, the shorter exposure times for bright field imaging mean reduced phototoxicity and further time savings on imaging.
- microscopy software
- cell culture monitoring
- cell culture optimization
- Cell Culture Technologies
- cell nucleus
- fluorescence markers
- artificial intelligence
- neural networks
- deep learning
- artificial neural networks
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