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Related Experiment Video

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An Analytical Tool that Quantifies Cellular Morphology Changes from Three-dimensional Fluorescence Images
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AUTOMATED CELL SEGMENTATION WITH 3D FLUORESCENCE MICROSCOPY IMAGES.

Jun Kong1, Fusheng Wang1, George Teodoro2

  • 1Department of Biomedical Informatics, Emory University, Atlanta, GA, 30322, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|September 26, 2015
PubMed
Summary

This study introduces an automated 3D cell segmentation method for fluorescence microscopy, crucial for cancer research. The technique accurately identifies individual cells and handles clustered cells, improving quantitative analysis.

Keywords:
3D Cell AnalysisFluorescence Microscopy ImageGradient Vector Flow

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Area of Science:

  • Biomedical Imaging
  • Computational Biology
  • Cancer Research

Background:

  • Quantitative analysis of cell biological properties in cancer research relies heavily on accurate cell segmentation in 3D fluorescence microscopy.
  • Existing methods often struggle with reliability and effectiveness for complex 3D imaging data.

Purpose of the Study:

  • To develop a fully automated and reliable cell segmentation method for 3D fluorescence microscopic images.
  • To enable precise quantitative analysis of cell properties in cancer investigations.

Main Methods:

  • Employed gradient vector flow (GVF) on interpolated and smoothed image data to regulate the image gradient field.
  • Grouped voxels by tracking GVF field and identified gradient modes.
  • Applied adaptive thresholding and a multiscale cell filter to enhance voxel intensities for segmentation.

Main Results:

  • Achieved low rates of false cell detection and missed cells for individual cell segmentation.
  • Demonstrated minimal over- and under-segmentation issues for clustered cells.
  • Showed encouraging concordance between automated and manual segmentation for cell morphometry.

Conclusions:

  • The developed automated 3D cell segmentation method is effective and reliable for fluorescence microscopic images.
  • This technique shows significant promise for advancing quantitative cell analysis in cancer studies.