Auto-detection of cervical collagen and elastin in Mueller matrix polarimetry microscopic images using K-NN and semantic segmentation classification.

We propose an approach for discriminating fibrillar collagen fibers from elastic fibers in the mouse cervix in Mueller matrix microscopy using convolutional neural networks (CNN) and K-nearest neighbor (K-NN) for classification. Second harmonic generation (SHG), two-photon excitation fluorescence (TPEF), and Mueller matrix polarimetry images of the mice cervix were collected with a self-validating Mueller matrix micro-mesoscope (SAMMM) system. The components and decompositions of each Mueller matrix were arranged as individual channels of information, forming one 3-D voxel per cervical slice. The classification algorithms analyzed each voxel and determined the amount of collagen and elastin, pixel by pixel, on each slice. SHG and TPEF were used as ground truths. To assess the accuracy of the results, mean-square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were used. Although the training and testing is limited to 11 and 5 cervical slices, respectively, MSE accuracy was above 85%, SNR was greater than 40 dB, and SSIM was larger than 90%.