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Related Experiment Video

Updated: May 22, 2026

Automated Quantification of Hematopoietic Cell &#8211; Stromal Cell Interactions in Histological Images of Undecalcified Bone
09:31

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

A fast, automatic segmentation algorithm for locating and delineating touching cell boundaries in imaged

X Qi1, F Xing, D J Foran

  • 11Department of Pathology and Laboratory Medicine, UMDNJ-Robert Wood Johnson Medical School, Piscataway, NJ 08854, USA. qixi@umdnj.edu

Methods of Information in Medicine
|April 25, 2012
PubMed
Summary

This study introduces an automated algorithm for separating overlapping cells in histology images, improving cancer diagnostics. The novel method achieves high accuracy and significantly faster processing using GPU acceleration.

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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging

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Area of Science:

  • Digital pathology
  • Computational biology
  • Medical imaging analysis

Background:

  • Automated histopathology analysis aids cancer detection and classification.
  • Automated cell segmentation in tissue microarray (TMA) images is crucial for quantitative analysis.
  • Overlapping cells present a significant challenge for conventional segmentation algorithms.

Purpose of the Study:

  • To develop a novel, automatic algorithm for separating overlapping cells in bright-field RGB histology images.
  • To enhance the reliability of cell segmentation in histopathology for cancer research and clinical applications.

Main Methods:

  • Image analysis identifies salient regions of interest based on visual content.
  • A voting-based seed detection method is employed for rapid identification.
  • A repulsive level set deformable model is utilized for precise cell contour generation.

Main Results:

  • The algorithm was tested on 100 image patches with over 1000 overlapping cells.
  • Achieved an overall precision of 90% and recall of 78% in cell segmentation.
  • GPU implementation demonstrated a 22x speedup compared to sequential C/C++ execution.

Conclusions:

  • The proposed algorithm accurately detects and separates overlapping cells in histology specimens.
  • Graphics Processing Units (GPU) offer an efficient platform for accelerating overlapping cell segmentation.