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Digital histology of gastric tissue biopsies with liquid crystal-based Mueller microscope and machine learning approach.
In: (Liquid Crystals Optics and Photonic Devices 2024, 8-11 April 2024, Strasbourg). 1000 20th St, Po Box 10, Bellingham, Wa 98227-0010 Usa: SPIE, 2024. DOI: 10.1117/12.3021846 (Proc. SPIE ; 13016)
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.
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Publication type
Article: Conference contribution
Keywords
Gastric Cancer ; Mueller Microscopy ; Optical Anisotropy ; Statistical Image Analysis
ISSN (print) / ISBN
0277-786X
e-ISSN
1996-756X
Conference Title
Liquid Crystals Optics and Photonic Devices 2024
Conference Date
8-11 April 2024
Conference Location
Strasbourg
Journal
Proceedings of SPIE
Quellenangaben
Volume: 13016
Publisher
SPIE
Publishing Place
1000 20th St, Po Box 10, Bellingham, Wa 98227-0010 Usa
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Biological and Medical Imaging (IBMI)
Grants
French Gastroenterology Society
ANR grant EMMIE
ANR grant EMMIE