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DeepLoco: Fast 3D Localization Microscopy Using Neural Networks

Nicholas Boyd, Eric Jonas, Hazen Babcock, Benjamin Recht
doi: https://doi.org/10.1101/267096
Nicholas Boyd
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Eric Jonas
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Hazen Babcock
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Benjamin Recht
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Abstract

Single-molecule localization super-resolution microscopy (SMLM) techniques like STORM and PALM have transformed cellular microscopy by substantially increasing spatial resolution. In this paper we introduce a new algorithm for a critical part of the SMLM process: estimating the number and locations of the fluorophores in a single frame. Our algorithm can analyze a 20000-frame experimental 3D SMLM dataset in about one second — substantially faster than real-time and existing algorithms. Our approach is straightforward but very different from existing algorithms: we train a neural network to minimize the Bayes’ risk under a generative model for single SMLM frames. The neural network maps a frame directly to a collection of fluorophore locations, which we compare to the ground truth using a novel loss function. While training the neural network takes several hours, it only has to be done once for a given experimental setup. After training, localizing fluorophores in new images is extremely fast — orders of magnitude faster than existing algorithms. Faster recovery opens the door to real-time calibration and accelerated acquisition, and future work could tackle more complicated optical systems and more realistic simulators.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted February 16, 2018.
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DeepLoco: Fast 3D Localization Microscopy Using Neural Networks
Nicholas Boyd, Eric Jonas, Hazen Babcock, Benjamin Recht
bioRxiv 267096; doi: https://doi.org/10.1101/267096
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DeepLoco: Fast 3D Localization Microscopy Using Neural Networks
Nicholas Boyd, Eric Jonas, Hazen Babcock, Benjamin Recht
bioRxiv 267096; doi: https://doi.org/10.1101/267096

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