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Quantitative comparison of spot detection methods in fluorescence microscopy.

Ihor Smal1, Marco Loog, Wiro Niessen

  • 1Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus MC, Rotterdam, The Netherlands. i.smal@erasmusmc.nl

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Summary

Automated spot detection in biological imaging is challenging, especially with low signal-to-noise ratios (SNR). Supervised machine learning methods outperform unsupervised approaches in low SNR conditions for accurate spot detection.

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Area of Science:

  • Biological imaging
  • Quantitative analysis
  • Fluorescence microscopy

Background:

  • Automated spot detection is crucial for quantitative analysis of biological images, particularly in live cell imaging.
  • Low signal-to-noise ratio (SNR) in fluorescence microscopy presents a significant challenge for accurate automated spot detection.
  • A lack of comprehensive quantitative evaluation exists for existing spot detection methods.

Purpose of the Study:

  • To evaluate and compare the performance of commonly used automated spot detection methods.
  • To assess methods under varying signal-to-noise ratio (SNR) conditions, from low to high.
  • To provide a benchmark for selecting appropriate spot detection techniques in biological imaging.

Main Methods:

  • Evaluated seven unsupervised and two supervised (machine learning) spot detection methods.
  • Conducted experiments on three types of synthetic images with ground truth data.
  • Validated performance on real biological image datasets from two studies using expert manual annotations.

Main Results:

  • Supervised methods demonstrated superior performance in very low SNR conditions (approx. 2).
  • Unsupervised methods, specifically h-dome transform and multiscale variance-stabilizing transform, showed comparable performance at low SNRs.
  • At high SNRs (approx. > 5), the performance differences among all tested detectors became insignificant.

Conclusions:

  • Machine learning-based supervised methods are recommended for automated spot detection in low SNR biological imaging.
  • Unsupervised methods offer a viable alternative when a learning stage is undesirable, performing comparably in low SNR scenarios.
  • Detector performance converges at high SNRs, making most methods suitable for such conditions.