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FISH - Fluorescent In-situ Hybridization02:07

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Fluorescence in situ hybridization, or FISH, was developed in the early 1980s and has quickly become one of the most widely used techniques in cytogenetics. Labeled probes are used to bind complementary DNA or RNA sequences on a chromosome or in a region within a cell. Earlier, the probes could only be obtained by cloning or reverse transcription of a DNA template. Currently, the probe oligonucleotides can be synthesized synthetically. Additionally, with the advancement of optical techniques,...
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Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae
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Fluorescence in Situ Hybridization Cell Image Segmentation Method.

Lemin Shi1,2, Xin Feng1, Ming Yue1

  • 1School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, 130022, China.

Studies in Health Technology and Informatics
|November 26, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an improved method for segmenting cells in fluorescence in situ hybridization (FISH) images, enhancing cancer diagnosis. The technique achieves high accuracy in identifying and segmenting cells for genetic analysis.

Keywords:
Cell recognitionFluorescence in situ HybridizationImage segmentation

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Area of Science:

  • Molecular Cytogenetics
  • Biomedical Imaging
  • Computational Biology

Background:

  • Fluorescence in situ hybridization (FISH) is crucial for diagnosing cancers and genetic disorders at the cellular level.
  • Accurate cell segmentation in FISH images is essential for quantifying DNA/RNA signals but is challenging due to image quality issues.
  • Low contrast, unclear boundaries, and cell defects in FISH images impede reliable cell identification and segmentation.

Purpose of the Study:

  • To develop and validate an advanced cell image segmentation method specifically for fluorescence in situ hybridization (FISH) applications.
  • To address the limitations of existing methods in handling poor image quality in FISH datasets.
  • To improve the accuracy and reliability of cell segmentation for downstream diagnostic analyses.

Main Methods:

  • The proposed method utilizes an improved adaptive histogram equalization technique for image enhancement.
  • A watershed segmentation approach is employed, using optimal segmentation feature values as seed points.
  • The method was applied to segment leukemia cells from fluorescence in situ hybridization images.

Main Results:

  • The developed method demonstrated high performance in segmenting 273 leukemia cells across 5 images.
  • Achieved a remarkable detection rate of 99% and an effectiveness of 94.5%.
  • Successfully addressed challenges posed by low contrast and unclear cell boundaries in FISH images.

Conclusions:

  • The proposed fluorescence in situ hybridization cell image segmentation method accurately segments cells, proving effective for diagnostic applications.
  • The technique offers a high detection rate and effectiveness, improving the reliability of FISH-based cellular analysis.
  • This advancement facilitates more precise quantification of genetic material within cells for cancer and genetic disease diagnosis.