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Area of Science:

  • Digital Pathology
  • Biomedical Image Analysis
  • Computational Imaging

Background:

  • Hematoxylin and eosin (H&E) staining is a cornerstone of histology, but offers limited molecular and structural information.
  • Multimodal imaging techniques can provide complementary data, potentially improving histopathological interpretation.
  • Quantitative methods are needed to objectively compare information content across different imaging modalities.

Purpose of the Study:

  • To develop and apply a quantitative framework for comparing the information content of H&E, multimodal imaging, and combined datasets.
  • To assess the information gain from individual channels within multimodal imaging, including Coherent Anti-Stokes Raman Scattering (CARS) microscopy spectral channels.
  • To establish a reproducible method for evaluating imaging approaches in digital pathology.

Main Methods:

  • Utilized deep learning and radiomics for feature extraction.
  • Implemented information markers including Shannon entropy, inverse area under the curve (1-AUC), and principal component analysis (PC95).
  • Compared information content of H&E, multimodal imaging, and their combination using Python 3.12.

Main Results:

  • The combined dataset consistently demonstrated higher information content across all metrics compared to H&E or multimodal imaging alone.
  • For instance, combined data achieved higher Shannon entropy (0.5740) versus H&E (0.5310) and multimodal (0.5385) using MobileNetV2 features.
  • The combined dataset required more principal components (62) to explain 95% of variance, compared to H&E (33) and multimodal (47).

Conclusions:

  • Combining multimodal imaging with H&E significantly increases the overall information content available for analysis.
  • This quantitative approach provides a reproducible framework for comparing and selecting optimal imaging strategies in digital pathology.
  • Multimodal imaging combinations hold substantial potential for enhancing image-based analyses in biomedical research.