Researchers at the Helmholtz Zentrum München have developed a new method for reconstructing continuous biological processes, such as disease progression, using image data. The study was published earlier this week in Nature Communications.
There is a decent amount of data that is shown in short cycles. Making this data more suitable for evaluation was the objective for Alexander Wolf, Ph.D. and his colleagues at the Helmholtz Zentrum München's Institute of Computational Biology (ICB).
"In the current study, we dealt with the problem that software cannot assign image data to continuous processes. For example, it is possible to classify image information according to clearly defined categories, but in disease progression and developmental biology, the limits are quickly reached because the processes are continuous and not individual steps," explains study leader Wolf.
To do this, the team used methods from so-called Deep Learning (i.e., machine learning processes). "Using artificial neural networks, we can now combine individual pictures into processes and additionally display them in a way that humans understand," said Philipp Eulenberg and Niklas Köhler, former Master's students at the ICB and the study's first authors.
In order to demonstrate the method's capability, the scientists selected two examples.
In the first approach, the software reconstructed the continuous cell cycle of white blood cells using images from an imaging flow cytometer (producing pictures in a fluorescence microscope). Two advantages of the software are that it is very fast and that it is six times less error prone compared to other approaches noted Wolf.
In the second approach, the researchers reconstructed the progress of diabetic retinopathy. The software was able to take 30,000 individual images of retinas as sparring partners and automatically compile them into a continues process so that disease progression could be observed, explains Köhler.
In addition to further applications for the method, in the future, Wolf and his colleagues want to solve other problems involving the evaluation of biological data using machine learning.
Caption: The new method is able to reconstruct biological processes using image data. Image courtesy of Helmholtz Zentrum München.