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A survey on cell nuclei instance segmentation and classification: Leveraging context and attention.

João D Nunes1, Diana Montezuma2, Domingos Oliveira3

  • 1INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, R. Dr. Roberto Frias, Porto, 4200-465, Portugal; University of Porto - Faculty of Engineering, R. Dr. Roberto Frias, Porto, 4200-465, Portugal.

Medical Image Analysis
|October 9, 2024
PubMed
Summary

Automated cell nuclei segmentation and classification in cancer imaging is challenging. Incorporating context and attention mechanisms into artificial neural networks shows promise for improving performance and generalization in analyzing whole slide images.

Keywords:
Artificial neural networksAttentionComputational pathologyContextNuclei instance segmentation and classification

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Area of Science:

  • Digital pathology
  • Computational imaging
  • Artificial intelligence in medicine

Background:

  • Manual annotation of cell nuclei in gigapixel Whole Slide Images (WSIs) is time-consuming and expensive.
  • Automated algorithms for nuclei instance segmentation and classification can aid pathologists and researchers.
  • Current algorithms struggle with high variability in nuclei features and staining artifacts.

Purpose of the Study:

  • To investigate the potential of context and attention mechanisms in artificial neural networks (ANNs) for improving nuclei instance segmentation and classification.
  • To review existing literature on context and attention methods in computer vision and medical imaging for nuclei analysis.
  • To identify challenges, limitations, and future research directions in this field.

Main Methods:

  • Conducted a comprehensive survey of context and attention mechanisms for cell nuclei analysis in H&E-stained microscopy images.
  • Extended Mask R-CNN and HoVer-Net with context and attention mechanisms.
  • Performed a comparative analysis on a multicenter dataset for colon nuclei identification and counting.

Main Results:

  • Findings suggest that translating pathologists' domain knowledge of context and attention into algorithm design is complex.
  • Context and attention mechanisms require further scientific understanding for effective implementation in ANNs.
  • The study provides insights into the advantages, use-cases, and limitations of these mechanisms.

Conclusions:

  • Integrating context and attention mechanisms into ANNs is a promising avenue for enhancing automated nuclei segmentation and classification.
  • Further research is needed to fully exploit these mechanisms and address current limitations.
  • Improved algorithms can significantly support digital pathology workflows and AI-driven cancer diagnostics.