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Multivariate analysis of Brillouin imaging data by supervised and unsupervised learning.

YuChen Xiang1, Kai Ling C Seow1, Carl Paterson1

  • 1Blackett Laboratory, Department of Physics, Imperial College London, London, UK.

Journal of Biophotonics
|March 6, 2021
PubMed
Summary

Multivariate algorithms offer a faster and more detailed approach to Brillouin imaging analysis compared to traditional line fitting. This advanced method enhances spectral unmixing, classification, and segmentation in complex biological samples.

Keywords:
Brillouinbioinformaticshyperspectral imagingmachine learningmultivariate

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Area of Science:

  • Biophotonics and advanced optical imaging techniques.
  • Spectroscopic analysis and data processing.

Background:

  • Brillouin imaging extracts spectral information from hyperspectral data, typically using line fitting.
  • Line fitting is sensitive to signal-to-noise ratio and struggles with spectral mixtures in complex samples.
  • Limitations exist in current Brillouin imaging for detailed analysis of biological specimens.

Purpose of the Study:

  • To introduce and evaluate multivariate algorithms for enhanced Brillouin imaging analysis.
  • To explore advanced applications like unmixing, classification, and segmentation in phantoms and live cells.
  • To improve the speed and detail of spectral parameter extraction in Brillouin microscopy.

Main Methods:

  • Application of supervised and unsupervised multivariate algorithms to hyperspectral Brillouin data.
  • Analysis of spectral mixtures using advanced computational techniques.
  • Comparison of multivariate analysis with traditional line fitting methods.
  • Imaging experiments conducted on a phantom and live cells.

Main Results:

  • Multivariate algorithms provide significantly enhanced contrast and detail in resulting images.
  • Analysis is approximately 100 times faster than conventional line fitting.
  • Estimated spectral parameters from multivariate analysis are consistent with pure fitting.
  • Successful demonstration of unmixing, classification, and segmentation in complex samples.

Conclusions:

  • Multivariate algorithms represent a powerful advancement for Brillouin imaging analysis.
  • These methods overcome limitations of traditional line fitting, especially in complex biological systems.
  • The proposed approach enables faster, more detailed, and robust spectral analysis for advanced imaging applications.