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Related Experiment Video

Updated: Jun 8, 2025

Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
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Data-driven nucleus subclassification on colon hematoxylin and eosin using style-transferred digital pathology.

Lucas W Remedios1, Shunxing Bao2, Samuel W Remedios3,4

  • 1Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States.

Journal of Medical Imaging (Bellingham, Wash.)
|November 7, 2024
PubMed
Summary

This study introduces a new AI method to identify previously unclassifiable cell types, like helper T cells and epithelial progenitors, on standard Hematoxylin and Eosin (H&E) stained tissue images, advancing histological analysis.

Keywords:
cell classificationdomain shifthematoxylin and eosinmultiplexed immunofluorescencevirtual hematoxylin and eosinvirtual staining

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Area of Science:

  • Computational pathology
  • Histology
  • Artificial intelligence in medicine

Background:

  • Hematoxylin and eosin (H&E) staining is standard for tissue analysis but struggles with detailed cell subtype classification.
  • Existing AI models have limitations in identifying specific epithelial, lymphocyte, and connective cell subtypes on H&E images.

Purpose of the Study:

  • To develop an inter-modality learning approach for classifying previously un-labelable cell types on H&E stained tissues.
  • To overcome limitations of current AI in identifying specific cell subtypes within epithelial, lymphocyte, and connective tissues.

Main Methods:

  • Utilized multiplexed immunofluorescence (MxIF) histology for detailed cell classification and annotation.
  • Employed style transfer to create synthetic H&E images from MxIF data.
  • Trained a supervised learning model on virtual H&E images and evaluated it on both virtual and real H&E datasets.

Main Results:

  • Achieved positive predictive values for classifying helper T cells and epithelial progenitors on virtual H&E.
  • Adapted classification for real H&E by matching predicted classes to coarser labels, yielding upper bound positive predictive values for these cell types.

Conclusions:

  • This research presents the first successful cell type classification for helper T and epithelial progenitor nuclei directly on H&E stained images.
  • The developed method enhances the capability of H&E staining for detailed cell subtype identification in histology.