We originally developed the ideas underlying the machine learning approach in a very different context, but through collaborating with experts in computational microscopy we were able to turn them into powerful methods for analyzing SMLM data
The DECODE (DEep COntext DEpendent) algorithm is based on deep learning: It uses a neural network that learns from training data. Instead of using real images, however, the network in this case is trained with synthetic data generated by a numerical simulation. By incorporating information about the microscopic setup and the imaging physics, the researchers achieved simulations that closely matched real-world acquisitions. “The neural network that we trained using simulated data can thus also detect and localize fluorophores in real images”, explains Artur Speiser, who, together with Lucas-Raphael Müller, was the lead author of the paper.
One of the benefits of DECODE is that it accurately detects and localizes fluorophores at higher densities than were previously possible. This means that fewer images are needed per sample. As a result, imaging speeds can be increased up to tenfold with minimal loss of resolution. In addition, DECODE can quantify uncertainties – so the network itself can detect when it is unsure of its localization. “This work is indicative of the approach of our Cluster of Excellence ‘Machine Learning: New Perspectives for Science’”, Macke says, whose chair is part of the Tübingen cluster. “We originally developed the ideas underlying the machine learning approach in a very different context, but through collaborating with experts in computational microscopy we were able to turn them into powerful methods for analyzing SMLM data.”
The team has also built a software package which implements the DECODE algorithm. “The software is simple to install and free to use, so we hope it will be useful for many scientists in the future”, adds Dr. Jonas Ries from EMBL.
Source: University of Tübingen
Source: Healthcare in Europe