Download our free white paper on instrument Quality Control in fluorescence bio-imaging
In a typical experiment in cell bio-imaging, each step can introduce variability into the results of the experiment. That’s why it is important to perform quality control at all these individual steps.
Any microscope introduces its own biases, and its performance can fluctuate over time. Being aware of the importance of controlling the quality of imaging instruments is crucial in the era of big data and artificial intelligence (machine learning, predictive models, etc.), where large volumes of data are generated and used to train the algorithms. If corrupted data is used to train the algorithms, corrupted results will be produced.
Contents
- – What are the four main biases that can be introduced by the microscope? What is their origin ?
- – What is the impact of field distortion on spatial measurements?
- – What is the impact of co-registration inaccuracy on colocalization quantification?
- – What is the impact of field non-uniformity on relative intensity measurements?
- – What is the impact of lateral resolution on counting objects?
- – How can these be assessed using Argolight solutions?
