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Molecular imaging with neural training of identification algorithm (neural network localization identification)
Author(s) -
Nelson A. J.,
Hess S. T.
Publication year - 2018
Publication title -
microscopy research and technique
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.536
H-Index - 118
eISSN - 1097-0029
pISSN - 1059-910X
DOI - 10.1002/jemt.23059
Subject(s) - artificial neural network , computer science , identification (biology) , artificial intelligence , graphics processing unit , algorithm , set (abstract data type) , data set , process (computing) , a priori and a posteriori , machine learning , pattern recognition (psychology) , philosophy , botany , epistemology , biology , programming language , operating system
Superresolution localization microscopy strongly relies on robust identification algorithms for accurate reconstruction of the biological systems it is used to measure. The fields of machine learning and computer vision have provided promising solutions for automated object identification, but usually rely on well represented training sets to learn object features. However, using a static training set can result in the learned identification algorithm making mistakes on data that is not well represented by the training set. Here, we present a method for training an artificial neural network without providing a training set in advance. This method uses the data to be analyzed, and the fitting algorithm to train an artificial neural network tailored to that data set. We show that the same artificial neural network can learn to identify at least two types of molecular emissions: the regular point spread functions (PSFs), and the astigmatism PSF. Simulations indicate that this method can be extremely reliable in extracting molecular emission signatures. Additionally, we implemented the artificial neural network calculation to be performed on a graphics processing unit (GPU) for massively parallelized calculation which drastically reduces the time required for the identification process. By implementing the neural identification on a GPU, we allow this method of identification to be used in a real time analysis algorithm. Research highlights Here, we present a machine learning algorithm for identifying point‐spread functions without the need for an a priori training set. We show that this method can detect over 90% of molecules with less than 1% false positive identification in simulations. We further show that because this algorithm does not make assumptions of about the shape of molecular emission, it is compatible with models beyond the symmetric 2D Gaussian.