Deep learning for dense and multicolor localization microscopy (Conference Presentation)

Elias Nehme; Eran Hershko; Lucien E. Weiss; Tomer Michaeli; Yoav Shechtman

doi:10.1117/12.2506499

13 March 2019 Deep learning for dense and multicolor localization microscopy (Conference Presentation)

Elias Nehme, Eran Hershko, Lucien E. Weiss, Tomer Michaeli, Yoav Shechtman

Proceedings Volume 10884, Single Molecule Spectroscopy and Superresolution Imaging XII; 108840R (2019) https://doi.org/10.1117/12.2506499
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

Deep learning has become an extremely effective tool for image classification and image restoration problems. Here, we address two fundamental problems of localization microscopy using machine learning: emitter density, and color determination. Modern microscopy can produce images of biological specimen at very high (super) resolution, by precisely determining the positions of numerous blinking light emitting molecules over time. To achieve fast acquisition time, a high density of molecules is required, which poses a significant challenge in terms of image processing. Existing approaches use elaborate algorithms with many parameters that require tuning and a long computation time. Here, we report an ultra-fast, precise, and parameter-free method for super-resolution microscopy that utilizes deep-learning: by feeding the computer images of dense molecules along with their correct positions, it is trained to automatically produce super-resolution images from blinking data. Next, we demonstrate how neural networks can exploit the chromatic dependence of the point-spread function to classify the colors of single emitters imaged on a grayscale camera. While existing single-molecule methods for spectral classification require additional optical elements in the emission path, e.g. spectral filters, prisms, or phase masks, our neural net correctly identifies static as well as mobile emitters with high efficiency using a standard, unmodified single-channel configuration. Finally, we demonstrate how deep learning can be used to design phase-modulating elements that, when implemented into the imaging path, result in further improved color differentiation between species.

Conference Presentation

Citation Download Citation

Elias Nehme, Eran Hershko, Lucien E. Weiss, Tomer Michaeli, and Yoav Shechtman "Deep learning for dense and multicolor localization microscopy (Conference Presentation)", Proc. SPIE 10884, Single Molecule Spectroscopy and Superresolution Imaging XII, 108840R (13 March 2019); https://doi.org/10.1117/12.2506499

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