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From WSI-level to patch-level: Structure prior-guided binuclear cell fine-grained detection.

Geng Hu1, Baomin Wang1, Boxian Hu1

  • 1School of Engineering Medicine, Beihang University, Beijing 100191, China; School of Biological Science, Beihang University and Key Laboratory of Biomechanics and Mechanobiology (Beihang University), Ministry of Education, Beijing 100191, China.

Medical Image Analysis
|August 16, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning method for accurate binuclear cell (BC) detection, improving leukemia risk prediction. The novel approach enhances automated analysis of microscopy images, overcoming limitations of manual counting and traditional methods.

Keywords:
Binuclear cellsCircular boundary boxesCytoplasm generatorMicroscopy whole-slide imagesTransformer

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Area of Science:

  • Computational pathology
  • Medical image analysis
  • Deep learning in oncology

Background:

  • Manual binuclear cell (BC) counting is time-consuming and subjective.
  • Traditional image processing struggles with staining variations and morphological diversity in BC microscopy whole-slide images (WSIs).
  • Accurate BC detection is crucial for predicting leukemia and other malignant tumors.

Purpose of the Study:

  • To develop an automated, accurate, and efficient method for binuclear cell (BC) detection and classification in whole-slide images (WSIs).
  • To overcome the limitations of manual counting and traditional image processing techniques for BC analysis.
  • To improve the prediction of leukemia and other malignant tumors through enhanced BC detection.

Main Methods:

  • A multi-task deep learning framework combining coarse detection (WSI level) and fine-grained classification (patch level).
  • Coarse detection utilizes circular bounding boxes for cells and key points for nuclei, offering rotation invariance.
  • Fine classification incorporates a background suppression module with color layer mask supervision and a transformer-based key region selection module. An unsupervised cytoplasm generator network was also developed.

Main Results:

  • The proposed method achieved superior performance compared to existing benchmarks across multiple evaluation criteria.
  • The circular bounding box representation proved effective for cell detection and robust to impurities.
  • Key point detection aided network perception and unsupervised segmentation, while the transformer enhanced classification accuracy.

Conclusions:

  • The developed deep learning method significantly improves binuclear cell (BC) detection accuracy and efficiency.
  • This approach offers a robust solution for analyzing BCs in whole-slide images (WSIs), addressing challenges in staining and morphology.
  • The findings support enhanced cancer screening and risk prediction through automated pathological analysis.