Speaker
Description
Deep learning is widely used in microscopy applications, mainly for processing the generated data, e.g. image segmentation and classification. Real-time applications are less common due to the research focus to develop the best possible method for a given problem and the time it requires to engineer. However, such approaches help in automating monotonous manual processes, they standardize the experiments and can multiply the number of measurements in the same amount of time. In my talk, I show two examples of automated microscopy systems where deep learning methods have a central role. The first project covers the automation of a patch clamp microscope that allows measuring the electrophysiological properties of neurons in human brain tissue slices. We use a contrast-enhancing technique to visualize the sample, and a trained deep learning method detects the healthy neurons in the tissue. Then, the system automatically moves a micropipette to the selected cell and performs the recording. The second project is about a system that automatically selects 3D cell cultures based on their morphological properties and transfers them into another plate. The included deep learning algorithm segments the cell cultures using stereo microscopy. This automates a sample preparation step when working with spheroids. Both of these systems have been validated and used for hundreds of measurements.
