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Related Experiment Video

Updated: Aug 25, 2025

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Singular Nuclei Segmentation for Automatic HER2 Quantification Using CISH Whole Slide Images.

Md Shakhawat Hossain1,2, M M Mahbubul Syeed2,3, Kaniz Fatema2,3

  • 1Department of CS, American International University-Bangladesh, Dhaka 1229, Bangladesh.

Sensors (Basel, Switzerland)
|October 14, 2022
PubMed
Summary

A new U-net based method accurately detects singular nuclei in breast cancer CISH images for HER2 quantification. This robust approach improves upon previous methods, enabling reliable HER2 grading for targeted therapy decisions.

Keywords:
HER2 gradingU-netdigital pathologynuclei segmentationwhole slide image

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Area of Science:

  • Oncology
  • Medical Imaging
  • Computational Pathology

Background:

  • Human epidermal growth factor receptor 2 (HER2) quantification is crucial for breast cancer treatment decisions.
  • Current methods like chromogenic in situ hybridization (CISH) require manual nuclei selection, which is time-consuming and prone to errors.
  • Existing automated methods using support vector machines (SVM) have limitations in accurately detecting singular nuclei.

Purpose of the Study:

  • To develop a robust automated method for singular nuclei detection in CISH whole slide images (WSIs) for accurate HER2 quantification.
  • To improve the reliability of HER2 grading for HER2-targeted therapy selection in breast cancer patients.
  • To compare the performance of the proposed method against a previously established SVM-based approach.

Main Methods:

  • A U-net based deep learning model was developed for singular nuclei detection.
  • Complementary color correction and deconvolution techniques were integrated to enhance image quality and feature extraction.
  • The method was applied to CISH whole slide images for HER2 quantification and grading.

Main Results:

  • The proposed U-net based method demonstrated robust detection of singular nuclei.
  • The automated HER2 quantification using the U-net model showed improved accuracy compared to the SVM-based method.
  • The integration of color correction and deconvolution contributed to enhanced detection performance.

Conclusions:

  • The developed U-net based method offers a reliable and accurate solution for automated HER2 quantification in breast cancer.
  • This approach has the potential to streamline the pathological workflow and improve the precision of HER2-targeted therapy selection.
  • Further validation on larger datasets is warranted to confirm its clinical utility.