Improved Cryosectioned Tissue Imaging Using Artificial Intelligence-Based Image Mapping

Tech ID: 30219 / UC Case 2019-450-0

Abstract

Researchers at the University of California, Davis have developed a process that utilizes artificial intelligence-based image mapping to improve the image of frozen tissue sections and reduce artifacts and distortion of those specimens.

Full Description

Frozen tissue sections are often used for rapid evaluation of fresh biological specimens. While freezing tissue eliminates the need for time-consuming formalin fixation and paraffin-embedding (FFPE) preparation, that technique also has the potential to introduce significant artifacts and distortions into a specimen’s image.

Artificial intelligence has emerged as a tool to improve resolution in microscopic imaging, as well as for rendering diagnoses and forecasting patient outcomes from digital pathology images. Researchers at the University of California, Davis have developed a process that utilizes artificial intelligence-based image mapping to improve the appearance of frozen specimens and reduce their artifacts and distortions. This technology maps or converts frozen-section images to closely resemble the appearance of FFPE-processed permanent specimens cut from a paraffin block.

Applications

  • Improved cryosectioned tissue processing and digital display

Features/Benefits

  • Eliminates the need for time-consuming FFPE-processing
  • Image conversion accomplished in sub-second times
  • Color normalization and correcting artifacts from freezing, tissue shrinkage and out-of-focus scans

Patent Status

Patent Pending

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Inventors

  • Fereidouni, Farzad
  • Levenson, Richard M.
  • Todd, Austin

Other Information

Keywords

tissue specimens, image mapping, diagnostic imaging, artificial intelligence, FFPE, formalin fixation, paraffin-embedding

Categorized As