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H&E image analysis pipeline for quantifying morphological features.

Valeria Ariotta1, Oskari Lehtonen1, Shams Salloum1,2

  • 1Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland.

Journal of Pathology Informatics
|November 2, 2023
PubMed
Summary
This summary is machine-generated.

We developed the hematoxylin and eosin (H&E) Image Processing pipeline (HEIP) for automated cell type detection in digital pathology. HEIP accurately segments cells in whole-slide images, revealing correlations between nuclear morphology and genomic data.

Keywords:
Digital pathologyFeature extractionInstance segmentationOvarian high-grade serous carcinomaPloidyWhole-slide images

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Area of Science:

  • Digital Pathology
  • Computational Biology
  • Histopathology Image Analysis

Background:

  • Automated cell type detection is crucial for digital pathology applications, especially with large whole-slide images (WSIs).
  • Existing methods require efficient pipelines for preprocessing, segmentation, and feature extraction from histopathological slides.

Purpose of the Study:

  • To introduce the hematoxylin and eosin (H&E) Image Processing pipeline (HEIP), an open-source software for automated analysis of H&E-stained WSIs.
  • To evaluate HEIP's performance in cell type detection and feature extraction for ovarian high-grade serous carcinoma (HGSC).

Main Methods:

  • HEIP software was developed as a flexible, modular pipeline for H&E slide analysis.
  • The pipeline includes preprocessing, instance segmentation, and nuclei feature extraction modules.
  • HEIP was applied to WSIs from HGSC patients for performance evaluation.

Main Results:

  • HEIP demonstrated high precision in instance segmentation, accurately identifying neoplastic and epithelial cells.
  • Significant correlations were found between genomic ploidy values and nuclear morphological features.
  • The software facilitates detailed analysis of cellular and genomic characteristics.

Conclusions:

  • HEIP provides an effective open-source solution for automated cell type detection and analysis in digital pathology.
  • The pipeline's ability to link morphological features with genomic data offers new avenues for cancer research.
  • HEIP enhances the efficiency and accuracy of histopathological image analysis.