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AUTOMATED CELL COUNTING AND CLUSTER SEGMENTATION USING CONCAVITY DETECTION AND ELLIPSE FITTING TECHNIQUES.

Sonal Kothari1, Qaiser Chaudry1, May D Wang2

  • 1Electrical and Computer Engineering, Georgia Institute of Technology.

Proceedings. IEEE International Symposium on Biomedical Imaging
|April 11, 2017
PubMed
Summary

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This study introduces a fast, semi-automatic method for precise cell cluster segmentation and counting in digital tissue images, improving pathological analysis.

Area of Science:

  • Digital Pathology
  • Computational Biology
  • Image Analysis

Background:

  • Complex cell clusters are characteristic of pathological conditions.
  • Accurate cell counting in these clusters is crucial for diagnosis.
  • Existing segmentation methods face challenges with complex cluster structures.

Purpose of the Study:

  • To develop a novel, fast, and semi-automatic method for cell cluster segmentation.
  • To enable accurate cell counting from digital tissue image samples.
  • To address the challenge of segmenting overlapping nuclei within clusters.

Main Methods:

  • A three-step process involving pre-processing, concavity detection, and ellipse-fitting segmentation.
  • Pre-processing generates nuclei cluster boundary images from RGB samples.

Related Experiment Videos

  • Concavity detection identifies overlapping nuclei points for precise segmentation.

    • Main Results:

    • The method demonstrated promising accuracy in segmenting and counting cells.
    • Tested on four types of cancerous tissue samples.
    • Achieved low percentage error, high true positive rate, and low false discovery rate.

    Conclusions:

    • The developed method offers an effective solution for cell cluster segmentation and counting.
    • It shows potential for improving diagnostic accuracy in digital pathology.
    • The technique is robust and applicable to various cancerous tissue types.