A fully automated quantitative analysis method based on deep learning algorithms for immunohistochemical staining expression intensities

Yongjian Deng; Bojun Cai; Xiaomei Wang

doi:10.1016/j.intonc.2025.06.001

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A fully automated quantitative analysis method based on deep learning algorithms for immunohistochemical staining expression intensities

更新时间：2026-06-03

- A fully automated quantitative analysis method based on deep learning algorithms for immunohistochemical staining expression intensities
- Intelligent Oncology Vol. 1, Issue 3, Pages: 256-264(2025)
- 作者机构：
  
  1. Hangzhou MediGenPro Biotechnology Co., Ltd.,Zhejiang,Hangzhou,China,311200
  2. Hangzhou PathoAI Technology Co., Ltd.,Zhejiang,Hangzhou,China,311100
- 作者简介：
- 基金信息：
- DOI：10.1016/j.intonc.2025.06.001
  CLC：
- Received：11 March 2025，
  
  Online First：30 June 2025，
  
  Published：26 July 2025
- 稿件说明：
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Yongjian Deng, Bojun Cai, Xiaomei Wang. A fully automated quantitative analysis method based on deep learning algorithms for immunohistochemical staining expression intensities[J]. Intelligent Oncology, 2025, 1(3): 256-264.
DOI：

Yongjian Deng, Bojun Cai, Xiaomei Wang. A fully automated quantitative analysis method based on deep learning algorithms for immunohistochemical staining expression intensities[J]. Intelligent Oncology, 2025, 1(3): 256-264. DOI： 10.1016/j.intonc.2025.06.001.

摘要

This paper focuses primarily on exploring the application of deep learning techniques and image processing algorithms in immunohistochemistry analysis

specifically targeting automated quantitative methods for nuclear

membrane

and cytoplasmic expressions of animal cells in whole-slide images. Cell nuclei

membranes

and cytoplasm were precisely identified and quantified by employing optical density separation techniques to differentiate between hematoxylin and 3

3'-diaminobenzidine staining components in combination with the CellViT nuclear segmentation algorithm and the region growing algorithm. Experimental validation demonstrates that the proposed algorithm performs excellently in terms of accuracy and recall. Compared to traditional manual interpretation

this algorithm achieve greater accuracy in specific quantitative metrics.

Abstract

This paper focuses primarily on exploring the application of deep learning techniques and image processing algorithms in immunohistochemistry analysis

specifically targeting automated quantitative methods for nuclear

membrane

and cytoplasmic expressions of animal cells in whole-slide images. Cell nuclei

membranes

and cytoplasm were precisely identified and quantified by employing optical density separation techniques to differentiate between hematoxylin and 3

this algorithm achieve greater accuracy in specific quantitative metrics.

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