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Kim, M. ; Lee, H.R.* ; Ossikovski, R.* ; Jobart-Malfait, A.* ; Lamarque, D.* ; Novikova, T.*

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)
Postprint DOI
Open Access Green
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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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Gastric Cancer ; Mueller Microscopy ; Optical Anisotropy ; Statistical Image Analysis
ISSN (print) / ISBN 0277-786X
e-ISSN 1996-756X
Konferenztitel Liquid Crystals Optics and Photonic Devices 2024
Konferzenzdatum 8-11 April 2024
Konferenzort Strasbourg
Zeitschrift Proceedings of SPIE
Quellenangaben Band: 13016 Heft: , Seiten: , Artikelnummer: , Supplement: ,
Verlag SPIE
Verlagsort 1000 20th St, Po Box 10, Bellingham, Wa 98227-0010 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Biological and Medical Imaging (IBMI)
Förderungen French Gastroenterology Society
ANR grant EMMIE