Raman spectroscopic histology using machine learning for nonalcoholic fatty liver disease.

Journal: FEBS letters
Published Date: Jul 8, 2019

Abstract

Histopathology requires the expertise of specialists to diagnose morphological features of cells and tissues. Raman imaging can provide additional biochemical information to benefit histological disease diagnosis. Using a dietary model of nonalcoholic fatty liver disease in rats, we combine Raman imaging with machine learning and information theory to evaluate cellular-level information in liver tissue samples. After increasing signal-to-noise ratio in the Raman images through superpixel segmentation, we extract biochemically distinct regions within liver tissues, allowing for quantification of characteristic biochemical components such as vitamin A and lipids. Armed with microscopic information about the biochemical composition of the liver tissues, we group tissues having similar composition, providing a descriptor enabling inference of tissue states, contributing valuable information to histological inspection.

Authors

  • Khalifa Mohammad Helal
    Graduate School of Life Science, Hokkaido University, Sapporo, Japan.
  • James Nicholas Taylor
    Research Center of Mathematics for Social Creativity, Institute for Electronic Science, Hokkaido University, Sapporo, Japan.
  • Harsono Cahyadi
    Department of Pathology and Cell Regulation, Kyoto Prefectural University of Medicine, Japan.
  • Akira Okajima
    Department of Gastroenterology and Hepatology, Kyoto Prefectural University of Medicine, Japan.
  • Koji Tabata
    Research Center of Mathematics for Social Creativity, Institute for Electronic Science, Hokkaido University, Sapporo, Japan.
  • Yoshito Itoh
    Department of Gastroenterology and HepatologyKyoto Prefectural University of MedicineKyotoJapan.
  • Hideo Tanaka
    Department of Pathology and Cell Regulation, Kyoto Prefectural University of Medicine, Japan.
  • Katsumasa Fujita
    Department of Applied Physics and the Advanced Photonics and Biosensing Open Innovation Laboratory (AIST); and the Transdimensional Life Imaging Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Osaka, Japan.
  • Yoshinori Harada
    Department of Pathology and Cell Regulation, Graduate School of Medical Science , Kyoto Prefectural University of Medicine , Kajii-cho, Kawaramachi-Hirokoji, Kyoto , 602-8566 , Japan.
  • Tamiki Komatsuzaki
    Research Institute for Electronic Science , Hokkaido University , Kita 20, Nishi 10 , Kita-ku, Sapporo 001-0020 , Japan.

Keywords

External Resources

Popular Topics
Recent Journals