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Deep learning-based image processing in optical microscopy.

Sindhoora Kaniyala Melanthota1, Dharshini Gopal2, Shweta Chakrabarti2

  • 1Department of Biophysics, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka 576104 India.

Biophysical Reviews
|May 9, 2022
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Summary

Deep learning (DL) enhances optical microscopy image analysis for biomedical research. This review explores DL applications in image processing, classification, and resolution enhancement, improving speed and accuracy.

Keywords:
Deep learningImage processingMachine learningOptical microscopy

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Area of Science:

  • Biomedical imaging
  • Optical microscopy
  • Computational biology

Background:

  • Optical microscopy is crucial for biomedical research, requiring high-resolution, high-contrast images with minimal sample damage.
  • Manual image analysis is time-consuming and prone to errors, necessitating automated methods.
  • Deep learning (DL) offers advanced image processing capabilities.

Purpose of the Study:

  • To review and critique the application of deep learning (DL) in optical microscopy image processing.
  • To highlight DL's role in enhancing image quality and enabling advanced analysis.
  • To explore DL's potential in making microscopy accessible for remote medical assistance.

Main Methods:

  • Systematic review of existing literature on DL applications in optical microscopy.
  • Analysis of DL techniques for image classification, segmentation, and resolution enhancement.
  • Evaluation of DL performance across various optical microscopy techniques.

Main Results:

  • DL significantly improves image resolution, contrast, and reduces noise in optical microscopy.
  • DL excels in image classification and segmentation tasks, aiding in feature identification.
  • DL-powered smartphone microscopy demonstrates potential for remote diagnostics.

Conclusions:

  • Deep learning is a powerful tool for advancing optical microscopy image analysis in biomedicine.
  • DL integration enhances efficiency, accuracy, and accessibility of microscopic data.
  • Future research should focus on further optimizing DL algorithms for diverse microscopy applications.