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Related Experiment Video

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Enhanced HoVerNet Optimization for Precise Nuclei Segmentation in Diffuse Large B-Cell Lymphoma.

Gei Ki Tang1, Chee Chin Lim1,2, Faezahtul Arbaeyah Hussain3,4

  • 1Faculty of Electronic Engineering and Technology, Universiti Malaysia Perlis, Arau 02600, Perlis, Malaysia.

Diagnostics (Basel, Switzerland)
|August 14, 2025
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Summary

HoVerNet accurately segments and classifies Diffuse Large B-Cell Lymphoma (DLBCL) nuclei in CMYC-stained images. Integrated into a GUI, it enhances diagnostic efficiency and real-time visualization for improved patient care.

Keywords:
HoVerNetdeep learningdiffuse large B-Cell lymphomagraphic user interfacenuclei classificationnuclei segmentation

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Area of Science:

  • Computational pathology
  • Digital histopathology
  • Artificial intelligence in oncology

Background:

  • Diffuse Large B-Cell Lymphoma (DLBCL) is the most common non-Hodgkin lymphoma, necessitating precise nuclei analysis for diagnosis and staging.
  • Accurate segmentation and classification of nuclei in whole slide images (WSIs) are crucial for effective DLBCL assessment.
  • Current diagnostic methods can be labor-intensive and subjective, highlighting the need for automated solutions.

Purpose of the Study:

  • To evaluate the performance of the HoVerNet deep learning model for nuclei segmentation and classification in CMYC-stained DLBCL WSIs.
  • To assess the integration of HoVerNet into a user-friendly graphic user interface (GUI) for diagnostic support.
  • To determine the model's efficacy in handling complex nuclei morphology and overlapping structures.

Main Methods:

  • A dataset of 122 CMYC-stained WSIs was utilized, undergoing stain normalization and patch extraction.
  • The HoVerNet multi-branch neural network was employed for nuclei segmentation and classification tasks.
  • Model performance was quantified using accuracy, precision, recall, and F1 score, with a GUI developed for practical application.

Main Results:

  • HoVerNet achieved a validation accuracy of 82.5%, with precision at 85.3%, recall at 82.6%, and an F1 score of 83.9%.
  • The model demonstrated robust performance in distinguishing overlapping and morphologically complex nuclei.
  • The integrated GUI facilitated real-time visualization, cell counting, and severity assessment, improving diagnostic workflow efficiency.

Conclusions:

  • HoVerNet, coupled with an integrated GUI, offers a promising advancement for streamlining DLBCL diagnostics.
  • The system provides accurate nuclei segmentation and real-time visualization, enhancing histopathological analysis.
  • Future research will explore Vision Transformers and additional staining methods to broaden clinical applicability and generalizability.