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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Generating 2.5D pathology for enhanced viewing and AI diagnosis.

Ekaterina Redekop1, Mara Pleasure2, Zichen Wang1

  • 1Department of Bioengineering, University of California, Los Angeles, USA.

Journal of Pathology Informatics
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PubMed
Summary

Pathologists can now view 2.5D biopsy cores, integrating serial tissue sections for better 3D spatial understanding. This novel framework aids deep learning models and improves tissue analysis for cancer grading.

Keywords:
2.5D biopsyDeep learningDigital pathologyDigital viewerProstate cancerVision transformer

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Area of Science:

  • Digital Pathology
  • Computational Anatomy
  • Medical Imaging Analysis

Background:

  • Histological analysis of biopsy samples involves evaluating complex 3D tissue structures across multiple slides, a process that is time-consuming due to manual zooming and panning.
  • Current deep learning models often analyze 2D cross-sections, failing to capture crucial 3D spatial information inherent in biopsy specimens.

Purpose of the Study:

  • To develop a novel framework for constructing 2.5D biopsy cores from serial tissue sections.
  • To enable enhanced visualization for pathologists and provide suitable input for advanced deep learning models, such as video transformers, for capturing depth-wide spatial dependencies.

Main Methods:

  • A novel morphology-preserving alignment framework was developed to extract and co-align serial tissue sections, creating 2.5D biopsy cores.
  • The framework was applied to construct 2.5D cores for a large cohort of prostate, breast, and renal biopsies.
  • Downstream task evaluation included training a deep learning-based cancer grading model and conducting a pathologist reader study using the 2.5D cores.

Main Results:

  • The framework successfully constructed 2.5D biopsy cores from over 10,000 prostate biopsies, along with breast and renal biopsies.
  • The 2.5D cores demonstrated utility in downstream tasks, including the development of a deep learning cancer grading model.
  • Pathologist reader studies indicated the potential for enhanced viewing and analysis using the 2.5D core representation.

Conclusions:

  • The developed framework effectively generates 2.5D biopsy cores, preserving tissue morphology and enabling the capture of 3D spatial information.
  • These 2.5D cores offer a valuable tool for both pathologist visualization and advanced computational analysis in digital pathology.
  • This approach represents a significant advancement in leveraging 3D tissue information for improved diagnostic accuracy and machine learning applications in histopathology.