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Related Experiment Video

Updated: May 28, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

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

Published on: April 8, 2016

Deep Learning-Based Positive Region Segmentation and Spatial Registration of Virtual Multiplex Immunohistochemical

Xiafei Shi1, Liuna Wang1, Dian Hu2

  • 1School of Life Sciences, Tiangong University, Tianjin, China.

Laboratory Investigation; a Journal of Technical Methods and Pathology
|May 26, 2026
PubMed
Summary

This study introduces a deep learning framework for virtual multiplexing of immunohistochemistry (IHC) images, enabling precise spatial reconstruction of multiple markers for tumor microenvironment analysis.

Keywords:
image registrationimage segmentationtumor microenvironment analysisvirtual multiplex immunohistochemistrywhole-slide images

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Related Experiment Videos

Last Updated: May 28, 2026

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
08:40

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Published on: April 8, 2016

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Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment
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Multiplex Immunofluorescence Combined with Spatial Image Analysis for the Clinical and Biological Assessment of the Tumor Microenvironment

Published on: June 2, 2023

Area of Science:

  • Computational pathology
  • Digital pathology
  • Biomedical image analysis

Background:

  • Virtual multiplexing allows for the simultaneous analysis of multiple biomarkers from single tissue slides.
  • Accurate identification and spatial reconstruction of positive regions in immunohistochemistry (IHC) images are crucial for understanding tumor microenvironment.
  • Current methods face challenges in precise segmentation and registration of multiple IHC markers.

Purpose of the Study:

  • To establish a deep learning-based analytical framework for precise identification and spatial reconstruction of positive regions in multiple IHC images.
  • To facilitate virtual multiplexing by integrating segmentation, registration, and reconstruction capabilities.
  • To provide a comprehensive multi-parameter spatial analysis tool for tumor microenvironment research.

Main Methods:

  • A Pyramid U-Net architecture was developed for efficient semantic segmentation of multiple IHC images.
  • A combination of SuperPoint, SuperGlue, and RANSAC algorithms was used for high-precision registration of virtual multiplex IHC.
  • The DINO-ViTS16-WSI algorithm was integrated to map IHC positive regions onto HE images for reconstruction and quantitative analysis.

Main Results:

  • The Pyramid U-Net model achieved high segmentation accuracy (e.g., 85.3% mIoU for Ki-67, 97.9% accuracy for ICAM-1).
  • Computational efficiency was improved 5.14-fold, with reduced inference time for TNF-α to 24.3 ms.
  • High-precision registration and spatial reconstruction were achieved, generating composite maps visualizing marker distribution.

Conclusions:

  • A deep learning framework enables accurate segmentation, registration, and spatial reconstruction of multiple IHC markers.
  • This framework provides crucial technical support for tumor microenvironment analysis.
  • The study supports personalized therapy through comprehensive multi-parameter spatial analysis.