Digital histology of gastric tissue biopsies with liquid crystal-based Mueller microscope and machine learning approach

Myeongseop Kim; Hee Ryung Lee; Razvigor Ossikovski; Aude Jobart-Malfait; Dominique Lamarque; Tatiana Novikova

doi:10.1117/12.3021846

18 June 2024 Digital histology of gastric tissue biopsies with liquid crystal-based Mueller microscope and machine learning approach

Myeongseop Kim, Hee Ryung Lee, Razvigor Ossikovski, Aude Jobart-Malfait, Dominique Lamarque, Tatiana Novikova

Author Affiliations +

Proceedings Volume 13016, Liquid Crystals Optics and Photonic Devices; 1301607 (2024) https://doi.org/10.1117/12.3021846
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

We investigated gastric tissue biopsies using a liquid crystal-based Mueller microscope and a machine-learning approach to examine the degree of inflammation. Machine learning and statistical analysis were performed with the multidimensional dataset including the polarimetric properties (linear retardance and dichroism, and circular depolarization) and total transmitted intensity images of the unstained thin sections of gastric tissue to identify and quantify the microstructural differences between healthy control, chronic gastritis, and gastric cancer.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Myeongseop Kim, Hee Ryung Lee, Razvigor Ossikovski, Aude Jobart-Malfait, Dominique Lamarque, and Tatiana Novikova "Digital histology of gastric tissue biopsies with liquid crystal-based Mueller microscope and machine learning approach", Proc. SPIE 13016, Liquid Crystals Optics and Photonic Devices, 1301607 (18 June 2024); https://doi.org/10.1117/12.3021846

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