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A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
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Molecular imaging with neural training of identification algorithm (neural network localization identification).

A J Nelson1, S T Hess1

  • 1Department of Physics and Astronomy, University of Maine, Orono, Maine, 04469-5709.

Microscopy Research and Technique
|September 23, 2018
PubMed
Summary

This study introduces a novel machine learning approach for identifying molecular emissions in superresolution microscopy without needing pre-existing training data. This method enhances accuracy and enables real-time analysis by tailoring artificial neural networks to specific datasets.

Keywords:
FPALMPALMSTORMconvolutional neural networkdeep learningmachine learningsemi-supervised learningsuperresolution localization microscopy

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Area of Science:

  • Biophysics
  • Computational Biology
  • Microscopy

Background:

  • Superresolution localization microscopy requires robust algorithms for accurate biological system reconstruction.
  • Existing machine learning methods often depend on predefined training sets, limiting adaptability to diverse data.
  • Static training sets can lead to identification errors when encountering data outside their representation.

Purpose of the Study:

  • To develop a method for training artificial neural networks (ANNs) without a priori training data for molecular emission identification.
  • To create an ANN tailored to specific datasets using the data itself and a fitting algorithm.
  • To enable real-time analysis of superresolution microscopy data through efficient computation.

Main Methods:

  • Training an ANN using the target dataset and a fitting algorithm, eliminating the need for a separate training set.
  • Utilizing graphics processing units (GPUs) for massively parallelized ANN calculations to reduce processing time.
  • Developing a method compatible with various molecular emission models, including non-symmetric ones.

Main Results:

  • The developed ANN successfully identified both regular point spread functions (PSFs) and astigmatism PSFs.
  • Simulations demonstrated high reliability in extracting molecular emission signatures.
  • The GPU implementation significantly accelerated the identification process, enabling real-time analysis.
  • The method achieved over 90% molecule detection with less than 1% false positives in simulations.
  • The algorithm's independence from emission shape assumptions broadens its applicability.

Conclusions:

  • This data-driven ANN training method offers a robust and adaptable solution for molecular emission identification in superresolution microscopy.
  • The GPU-accelerated approach facilitates real-time analysis, advancing the capabilities of biological imaging.
  • The algorithm's flexibility with different PSF models enhances its utility across various research applications.