5 March 2021Auto-detection of cervical collagen and elastin in Mueller Matrix polarimetry microscopic images using K-NN and Semantic Segmentation classification
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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.
Camilo Roa,Vinh Nguyen Du Le, andJessica 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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Camilo Roa, Vinh Nguyen Du Le, 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