Auto-detection of cervical collagen and elastin in Mueller Matrix polarimetry microscopic images using K-NN and Semantic Segmentation classification

Camilo Roa; Vinh Nguyen Du Le; Jessica Ramella-Roman

doi:10.1117/12.2578997

5 March 2021 Auto-detection of cervical collagen and elastin in Mueller Matrix polarimetry microscopic images using K-NN and Semantic Segmentation classification

Camilo Roa, Vinh Nguyen Du Le, Jessica Ramella-Roman

Author Affiliations +

Proceedings Volume 11646, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics; 116460Z (2021) https://doi.org/10.1117/12.2578997
Event: SPIE BiOS, 2021, Online Only

Abstract

Along with second harmonic generation and two-photon excited fluorescence measured with Non-Linear Microscopy, polarization properties measured with Mueller Matrix Polarimetry Microscopy can improve our understanding of the remodeling process in preterm pregnancy. This is critical to define therapeutic targets and to develop clinical tools for early and accurate detection of preterm risks. While manual analyzing and classifying individual cervical samples is time-consuming, automated algorithms can be advantageous when the number of samples is large. To such extent, we demonstrate the use of Convolutional Neural Networks (CNN) for feature extraction and K-Nearest Neighbor (KNN) for classification as an alternative to manual assessment.

Conference Presentation

Citation Download Citation

Camilo Roa, Vinh Nguyen Du Le, and Jessica Ramella-Roman "Auto-detection of cervical collagen and elastin in Mueller Matrix polarimetry microscopic images using K-NN and Semantic Segmentation classification", Proc. SPIE 11646, Polarized Light and Optical Angular Momentum for Biomedical Diagnostics, 116460Z (5 March 2021); https://doi.org/10.1117/12.2578997

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