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An automatic processing framework for hyperspectral histologic images and benchmark dataset.

Ling Ma1,2, Amie Ha1,2, Ifrah Zainab1,2

  • 1Center for Imaging and Surgical Innovation, University of Texas at Dallas, Richardson, TX.

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Summary
This summary is machine-generated.

This study introduces an automated pipeline for hyperspectral imaging (HSI) in histopathology. It accurately aligns HSI data with whole-slide images, enabling advanced digital pathology for cancer diagnosis.

Keywords:
Hyperspectral imaginghistologymicroscopepathologypreprocessing frameworktemplate matching

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Area of Science:

  • Digital Pathology
  • Histopathology
  • Medical Imaging

Background:

  • Hyperspectral imaging (HSI) is a promising tool for histopathology.
  • Current validation relies on RGB images, necessitating correlation with whole-slide images.
  • Accurate tissue labeling is crucial for hyperspectral data utilization.

Purpose of the Study:

  • To develop an automated pipeline for processing hyperspectral histological images.
  • To enable precise localization, cropping, and alignment of HSI tiles with whole-slide images.
  • To facilitate the creation of comprehensive datasets for digital pathology.

Main Methods:

  • Developed a fully automated processing pipeline.
  • Integrated whole-slide images, annotations, and HSI tiles.
  • Implemented algorithms for locating HSI regions, cropping relevant data, and aligning image components.

Main Results:

  • Successfully processed over 350 whole-slide hyperspectral histological images of head and neck cancers.
  • Created a robust dataset for further research.
  • Demonstrated the pipeline's capability to accurately correlate HSI data with corresponding regions in whole-slide images.

Conclusions:

  • The automated pipeline effectively processes hyperspectral histological images.
  • This tool facilitates the integration of HSI into digital pathology workflows.
  • Enables automated histology diagnosis and advances cancer research.