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Related Experiment Video

Updated: Sep 8, 2025

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
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Clustering Initiated Multiphase Active Contours and Robust Separation of Nuclei Groups for Tissue Segmentation.

Adel Hafiane1, Filiz Bunyak1, Kannappan Palaniappan1

  • 1Dept. of Computer Science, University of Missouri-Columbia, Columbia, MO 65211 USA.

Proceedings of the ... IAPR International Conference on Pattern Recognition. International Conference on Pattern Recognition
|June 13, 2022
PubMed
Summary

This study introduces a new automated method for grading cancer tissues from biopsies. The computer-assisted approach achieves a 91% accuracy in detecting cell nuclei, improving quantitative pathology.

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Area of Science:

  • Computational pathology
  • Digital image analysis
  • Cancer diagnostics

Background:

  • Automated histological grading of cancer tissue biopsies is crucial for clinical care but remains challenging.
  • Current methods lack sophisticated algorithms for accurate image segmentation, feature extraction, and classification.
  • There is a need for automatic image-based grading systems for quantitative pathology.

Purpose of the Study:

  • To develop a novel, automated approach for histological tissue segmentation and nuclei detection in cancer biopsies.
  • To improve the accuracy and efficiency of quantitative pathology for cancer grading.

Main Methods:

  • Utilized fuzzy spatial clustering for tissue segmentation.
  • Employed vector-based multiphase level set active contours.
  • Implemented an iterative kernel voting scheme for nuclei detection, robust to clumped nuclei.

Main Results:

  • Achieved a 91% detection rate for cell nuclei centers compared to manual ground truth.
  • Demonstrated robustness in nuclei detection even with clumped and touching nuclei.
  • Successfully applied the method across various prostate cancer grades.

Conclusions:

  • The novel approach shows significant promise for automated histological grading in cancer diagnostics.
  • This method can enhance quantitative pathology by providing accurate and reliable image-based analysis.
  • Further development could lead to the first automatic image-based grading system for cancer tissues.