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DETECTOR: structural information guided artifact detection for super-resolution fluorescence microscopy image.

Shan Gao1,2,3, Fan Xu4,3, Hongjia Li1,2

  • 1High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.

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|October 25, 2021
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Summary
This summary is machine-generated.

We developed DETECTOR, a new method for identifying artifacts in super-resolution microscopy images. It accurately detects structural inaccuracies, especially in challenging images with background noise or from single molecule localization microscopy.

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Area of Science:

  • * Advanced optical microscopy and bioimaging.
  • * Nanoscale visualization of cellular structures.

Background:

  • * Super-resolution fluorescence microscopy enables nanoscale imaging of subcellular structures.
  • * Reliable artifact detection is crucial for validating super-resolution image accuracy.
  • * Existing methods often produce false positives due to intensity-based comparisons.

Purpose of the Study:

  • * To introduce DETECTOR, a novel artifact detection method for super-resolution images.
  • * To improve artifact detection by focusing on structural information rather than intensity.
  • * To address limitations of current methods, particularly in challenging imaging conditions.

Main Methods:

  • * Proposed DETECTOR, a structural information-guided artifact detection approach.
  • * Computed structural dissimilarity between wide-field and super-resolution images.
  • * Introduced MASK-SSIM (Masked Structural Similarity Index Measure) to focus on structural similarity and mitigate background noise.

Main Results:

  • * DETECTOR demonstrated superior performance in detecting structural artifacts compared to state-of-the-art methods.
  • * The method excels with strong autofluorescence backgrounds and single molecule localization microscopy (SMLM) data.
  • * Achieved high sensitivity in weak signal regions and potential to guide image acquisition and reconstruction.

Conclusions:

  • * DETECTOR offers a robust and reliable solution for artifact detection in super-resolution microscopy.
  • * The structural comparison approach overcomes limitations of intensity-based methods.
  • * This method enhances the trustworthiness of nanoscale imaging and aids in optimizing imaging protocols.