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Published on: November 15, 2017
DeepSinse: deep learning-based detection of single molecules
John S H Danial1,2, Raed Shalaby3, Katia Cosentino4
1Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK.
Motivation:
Imaging single molecules has emerged as a powerful characterization tool in the biological sciences. The detection of these under various noise conditions requires the use of algorithms that are dependent on the end-user inputting several parameters, the choice of which can be challenging and subjective.
Results:
In this work, we propose DeepSinse, an easily trainable and useable deep neural network that can detect single molecules with little human input and across a wide range of signal-to-noise ratios. We validate the neural network on the detection of single bursts in simulated and experimental data and compare its performance with the best-in-class, domain-specific algorithms.
Availabilityand Implementation:
Ground truth ROI simulating code, neural network training, validation code, classification code, ROI picker, GUI for simulating, training and validating DeepSinse as well as pre-trained networks are all released under the MIT License on www.github.com/jdanial/DeepSinse. The dSTORM dataset processing code is released under the MIT License on www.github.com/jdanial/StormProcessor.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
