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FalseColor-Python: A rapid intensity-leveling and digital-staining package for fluorescence-based slide-free digital

Robert Serafin1, Weisi Xie1, Adam K Glaser1

  • 1Department of Mechanical Engineering, University of Washington, Seattle, Washington, United States of America.

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Summary

This study introduces an open-source software package for digital pathology, enabling automated conversion of fluorescence images to hematoxylin and eosin (H&E) like colors. This method ensures consistent, high-quality digital staining for 2D and 3D microscopy, overcoming variability issues.

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Area of Science:

  • Digital pathology
  • Microscopy
  • Computational imaging

Background:

  • Slide-free digital pathology and 3D microscopy offer alternatives to traditional histology.
  • Current fluorescence-to-color conversion methods often require manual adjustments and yield inconsistent results due to staining variability.
  • Clinical adoption of advanced imaging techniques is hindered by the lack of robust, automated digital staining solutions.

Purpose of the Study:

  • To develop an open-source, automated software package for converting fluorescence microscopy images to a hematoxylin and eosin (H&E) color space.
  • To address limitations of existing false-coloring algorithms, specifically manual parameter tuning and inconsistency.
  • To provide a robust and scalable solution for digital staining in 2D and 3D microscopy.

Main Methods:

  • Developed a Python-based package for rapid intensity leveling and digital staining of two-channel fluorescence images.
  • Implemented automated false-coloring algorithms robust to intra- and inter-specimen intensity variations.
  • Utilized GPU acceleration for efficient processing of large 2D and 3D microscopy datasets.

Main Results:

  • Achieved automated and uniform false coloring, even with uneven staining in large specimens.
  • Generated consistent H&E-like color-space representations robust to staining and imaging variations.
  • Demonstrated successful application to 3D fluorescently imaged cleared tissues using open-top light-sheet microscopy.

Conclusions:

  • The open-source package provides a significant advancement for slide-free digital pathology, enabling automated and consistent H&E-like digital staining.
  • The software overcomes key challenges in fluorescence image conversion, facilitating clinical adoption of 3D microscopy techniques.
  • This platform offers a scalable and adaptable solution for various digital staining needs in scientific research and diagnostics.