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首页> 美国卫生研究院文献>Biomedical Optics Express >Auto-detection of cervical collagen and elastin in Mueller matrix polarimetry microscopic images using K-NN and semantic segmentation classification
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Auto-detection of cervical collagen and elastin in Mueller matrix polarimetry microscopic images using K-NN and semantic segmentation classification

机译：使用K-NN和语义分割分类自动检测穆勒矩阵偏振偏振片微观图像中的颈椎胶原和弹性蛋白

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摘要

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%.
机译：我们提出了一种方法，用于使用卷积神经网络（CNN）和K最近邻（K-NN）在Mueller基质显微镜中从小鼠子宫颈中的弹性纤维鉴别纤维纤维的方法。用自验证橡胶矩阵微丘腔（SAMMM）系统收集小鼠子宫颈的二谐谐波（SHG），双光子激发荧光（TPEF）和Mueller基质偏振物图像。每个橡胶基质的组分和分解被排列为单独的信息通道，每个宫颈切片形成一个3-D体素。分类算法分析了每个体素并确定了每个切片上的胶原蛋白和弹性蛋白，像素的量。 SHG和TPEF被用作地面真理。为了评估结果的准确性，使用均方误差（MSE），峰值信噪比（PSNR）和结构相似度（SSIM）。虽然培训和测试仅限于11和5个宫颈切片，但MSE精度分别高于85％，SNR大于40 dB，SSIM大于90％。

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