| Issue |
J. Eur. Opt. Society-Rapid Publ.
Volume 18, Number 2, 2022
|
|
|---|---|---|
| Article Number | 10 | |
| Number of page(s) | 8 | |
| DOI | https://doi.org/10.1051/jeos/2022011 | |
| Published online | 23 November 2022 | |
Research Article
Optical diagnosis of gastric tissue biopsies with Mueller microscopy and statistical analysis
1
LPICM, CNRS, Ecole Polytechnique, IP Paris, LPICM, Palaiseau 91128, France
2
Université Paris-Saclay, Université de Versailles St. Quentin-en-Yvelines, INSERM (UMR 1173), Montigny-Le-Bretonneux, France
3
Université de Versailles St-Quentin en Yvelines, Hôpital Ambroise Paré, Service d’Hépato-Gastroentérologie, Boulogne Billancourt, France
4
Florida International University, Department of Biomedical Engineering, Miami, FL, USA
* Corresponding author: tatiana.novikova@polytechnique.edu
Received:
28
March
2022
Accepted:
20
October
2022
We investigate a possibility of producing the quantitative optical metrics to characterize the evolution of gastric tissue from healthy conditions via inflammation to cancer by using Mueller microscopy of gastric biopsies, regression model and statistical analysis of the predicted images. For this purpose the unstained sections of human gastric tissue biopsies at different pathological conditions were measured with the custom-built Mueller microscope. Polynomial regression model was built using the maps of transmitted intensity, retardance, dichroism and depolarization to generate the predicted images. The statistical analysis of predicted images of gastric tissue sections with multi-curve fit suggests that Mueller microscopy combined with data regression and statistical analysis is an effective approach for quantitative assessment of the degree of inflammation in gastric tissue biopsies with a high potential in clinical applications.
Key words: Mueller microscopy / Optical anisotropy / Statistical image analysis / Gastric cancer
© The Author(s), published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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