DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks
Journal article, 2021

Conventional analysis of fluorescence recovery after photobleaching (FRAP) data for diffusion coefficient estimation typically involves fitting an analytical or numerical FRAP model to the recovery curve data using non-linear least squares. Depending on the model this can be time-consuming, especially for batch analysis of large numbers of data sets and if multiple initial guesses for the parameter vector are used to ensure convergence. In this work, we develop a completely new approach, DeepFRAP, utilizing machine learning for parameter estimation in FRAP. From a numerical FRAP model developed in previous work, we generate a very large set of simulated recovery curve data with realistic noise levels. The data is used for training different deep neural network regression models for prediction of several parameters, most importantly the diffusion coefficient. The neural networks are extremely fast and can estimate the parameters orders of magnitude faster than least squares. The performance of the neural network estimation framework is compared to conventional least squares estimation on simulated data, and found to be strikingly similar. Also, a simple experimental validation is performed, demonstrating excellent agreement between the two methods. We make the data and code used publicly available to facilitate further 34
development of machine learning-based estimation in FRAP.


deep neural network


fluorescence recovery after photobleaching

37 machine learning

confocal laser scanning microscopy

deep learning


Victor Wåhlstrand Skärström

RISE Research Institutes of Sweden

Annika Krona

RISE Research Institutes of Sweden

Niklas Lorén

RISE Research Institutes of Sweden

Chalmers, Physics, Nano and Biophysics

Magnus Röding

University of Gothenburg

RISE Research Institutes of Sweden

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Journal of Microscopy

0022-2720 (ISSN) 1365-2818 (eISSN)

Vol. 282 2 146-161

Areas of Advance

Nanoscience and Nanotechnology (SO 2010-2017, EI 2018-)

Life Science Engineering (2010-2018)

Materials Science

Subject Categories

Other Physics Topics

Probability Theory and Statistics


Chalmers Materials Analysis Laboratory





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