Polarimetric imaging of cervical pre-cancer aided by machine learning (Conference Presentation)

Alexander Doronin; Demelza Robinson; Bastiaan W. Kleijn; Jean Rehbinder; Jeremy Vizet; Angelo Pierangelo; Tatiana Novikova

doi:10.1117/12.2649405

15 March 2023 Polarimetric imaging of cervical pre-cancer aided by machine learning (Conference Presentation)

Alexander Doronin, Demelza Robinson, Bastiaan W. Kleijn, Jean Rehbinder, Jeremy Vizet, Angelo Pierangelo, Tatiana Novikova

Author Affiliations +

Proceedings Volume PC12382, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics 2023; PC1238204 (2023) https://doi.org/10.1117/12.2649405
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

This report considers the development of several Machine Learning (ML) classifiers for the automatic diagnostic segmentation of Mueller Matrix polarimetric images of the uterine cervix. The analysed dataset includes polarimetric images obtained on 23 conization specimens in an ex vivo study conducted at the Kremlin-Bicêtre University Hospital in France. We demonstrate high level (>98%) accuracy for producing spatial masks of CIN/healthy zones regions when comparing directly with the pathological evaluated ground truth. The detailed results of numerous data tests, ML classifier training processes and avenues for improving the sensitivity and specificity of the developed techniques for “unseen” images are presented.

Conference Presentation

Citation Download Citation

Alexander Doronin, Demelza Robinson, Bastiaan W. Kleijn, Jean Rehbinder, Jeremy Vizet, Angelo Pierangelo, and Tatiana Novikova "Polarimetric imaging of cervical pre-cancer aided by machine learning (Conference Presentation)", Proc. SPIE PC12382, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics 2023, PC1238204 (15 March 2023); https://doi.org/10.1117/12.2649405

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KEYWORDS

Polarimetry

Machine learning

Biomedical optics

Diagnostics

Image segmentation

Cervical cancer

Cervix

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