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Automatic single cell segmentation on highly multiplexed tissue images.

Peter J Schüffler1,2, Denis Schapiro2,3, Charlotte Giesen3

  • 1Department of Computer Science, ETH Zurich, Zurich, 8092, Switzerland.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology
|July 7, 2015
PubMed
Summary
This summary is machine-generated.

Next-generation immunohistochemistry (IHC) uses multiplexing for detailed tissue analysis. This study introduces a new method using multiple markers for improved cell segmentation in multiplexed IHC images, enhancing data analysis.

Keywords:
cell segmentationmass cytometrymultiplexed imagingsingle cell proteomics

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Area of Science:

  • Computational pathology
  • Biomedical imaging
  • Proteomics

Background:

  • Mass cytometry and immunohistochemistry (IHC) enable simultaneous visualization of dozens of proteins and modifications in single tissue sections.
  • This multiplexing capability, combined with spatial information and quantification, represents a significant advancement in histopathological imaging, termed next-generation IHC.
  • Accurate and high-throughput cell segmentation is essential for analyzing complex multiplexed IHC data.

Purpose of the Study:

  • To develop a robust and accurate cell segmentation method for multiplexed histopathological images.
  • To demonstrate the advantage of using multiple markers for improved segmentation power compared to single-marker approaches.
  • To provide an open-source toolbox for automatic segmentation of multiplexed IHC data.

Main Methods:

  • A watershed-based cell segmentation algorithm was developed.
  • The algorithm utilizes a nuclear marker and multiple membrane markers, with automatic selection of membrane markers based on correlation.
  • The proposed method was compared against state-of-the-art segmentation pipelines that use only a single marker.

Main Results:

  • The use of multiple markers significantly increased the segmentation power compared to single-marker methods.
  • Multiplexed information is crucial and should not be ignored during the segmentation process.
  • A novel, user-friendly, open-source toolbox for automatic segmentation of multiplexed histopathological images was developed and is provided.

Conclusions:

  • Integrating multiple markers in cell segmentation substantially enhances the analysis of multiplexed histopathological images.
  • The developed watershed-based method and open-source toolbox offer a powerful solution for next-generation IHC data analysis.
  • This approach facilitates more accurate and comprehensive insights from complex tissue imaging.