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Paint by numbers: Algorithm reconstructs processes from individual images

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 in ‘Nature Communications’.
Modern life sciences generate a constantly growing amount of data in shorter and shorter cycles. Making such data controllable and suitable for evaluation is the objective of Dr. Dr. Alexander Wolf and his colleagues at the Helmholtz Zentrum München’s Institute of Computational Biology (ICB). With this in mind, the researchers are attempting to develop software that handles this evaluation. But of course there are various hurdles to clear.

“In the current study, we dealt with the problem that software cannot assign image data to continuous processes,” explains study leader Wolf. “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.”

In order to take this into account, the Helmholtz team employed 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,” say Philipp Eulenberg and Niklas Köhler, former Master’s students at the ICB and the study’s first authors.”

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