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Standardized Instance-Level Quantification of CD34-Positive Vessels in Lymph Node Whole-Slide Images Using U-Net.

Satomi Omotani1, Natsumi Yonemoto1, Akifumi Muramoto1

  • 1Department of Pathology, Faculty of Medical Sciences, University of Fukui, Eiheiji, Fukui, Japan.

Pathology International
|May 12, 2026
PubMed
Summary

This study introduces an instance-level deep learning method for accurately counting blood vessels, including high endothelial venules (HEVs), in lymph node whole-slide images (WSIs). The approach enables standardized quantification for diverse lymph node conditions.

Keywords:
AICD34deep learninglymph nodesvessels

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Area of Science:

  • Biomedical Image Analysis
  • Computational Pathology
  • Deep Learning in Histopathology

Background:

  • Quantitative analysis of blood vessels in whole-slide images (WSIs) is crucial for disease understanding.
  • Existing deep learning methods for vessel counting primarily use semantic segmentation, which may not be optimal for complex structures like lymph node high endothelial venules (HEVs).
  • Systematic evaluation of vessel counting in lymph nodes, particularly HEVs, using artificial intelligence (AI) is lacking.

Purpose of the Study:

  • To develop and evaluate an instance-level deep learning approach for detecting and counting individual CD34-positive vessels in lymph node WSIs.
  • To address the gap in systematic evaluation of AI-based vessel quantification in lymph nodes.
  • To provide a standardized method for quantifying lymph node vascular morphology.

Main Methods:

  • Developed a convolutional neural network (CNN) based on the U-Net architecture for biomedical image segmentation.
  • Trained the CNN on 4.0 million iterations to quantify CD34-positive vessels in lymph node WSIs.
  • Employed a fixed post-processing procedure to extract individual vessel instances and applied an instance-level detection approach.

Main Results:

  • The trained model, particularly at the 3.0 million iteration checkpoint, demonstrated the highest fidelity in capturing CD34-positive vascular morphology and morphological similarity in the validation set.
  • The instance-level detection performance was robust, showing minimal variation across different lymph nodes and training checkpoints.
  • The method achieved accurate vessel detection and counting in lymph node WSIs using a straightforward, fixed procedure.

Conclusions:

  • The developed instance-level deep learning model provides accurate and robust vessel detection and counting in lymph node WSIs.
  • This approach offers a practical foundation for standardized quantification of lymph node vasculature.
  • The model facilitates the study of lymph node vascular morphology in normal, inflammatory, and lymphomatous conditions.