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MOTH: Memory-Efficient On-the-Fly Tiling of Histological Image Annotations Using QuPath.

Thomas Kauer1, Jannik Sehring1, Kai Schmid1

  • 1Institute of Neuropathology, Justus-Liebig-University Giessen, Arndtstr. 16, 35392 Giessen, Germany.

Journal of Imaging
|November 26, 2024
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Summary
This summary is machine-generated.

This study introduces a toolkit for seamless data extraction from QuPath projects for artificial intelligence (AI) analysis of digital histopathology images. It streamlines AI workflows by enabling on-the-fly annotation tile extraction and result visualization.

Keywords:
artificial intelligencedigital pathologyqupathsegmentationwhole slide image

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Area of Science:

  • Digital Pathology
  • Computational Pathology
  • Artificial Intelligence in Medicine

Background:

  • Digitalized histopathological images offer new data analysis possibilities.
  • Artificial intelligence (AI) algorithms can automatically detect and analyze features in whole slide images.
  • Manual data extraction from annotation tools like QuPath for AI training is time-consuming.

Purpose of the Study:

  • To develop a toolkit for efficient data extraction from QuPath projects for AI algorithms.
  • To facilitate the on-the-fly extraction of annotation tiles for AI model training.
  • To integrate AI analysis results back into QuPath for visual inspection.

Main Methods:

  • Development of a toolkit for integration with existing AI pipelines (e.g., U-net).
  • On-the-fly extraction of annotation tiles from QuPath projects.
  • Direct transfer of AI results back to QuPath for visualization.

Main Results:

  • The toolkit enables direct use of QuPath annotation tiles as input for AI algorithms.
  • Results from AI algorithms can be directly visualized within QuPath.
  • Streamlined workflow for incorporating QuPath into AI-driven histopathology analysis.

Conclusions:

  • The developed toolkit significantly simplifies the integration of QuPath into AI workflows.
  • On-the-fly data extraction and result visualization enhance the efficiency of digital pathology analysis.
  • This approach facilitates the use of AI for automated analysis of histopathological data.