Dong Yang, Liu Shaoxiong, Shen Yuanxing, He Honghui, Ma Hui
Center for Precision Medicine and Healthcare, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen 518071, China.
These authors contributed equally to this work.
Biomed Opt Express. 2020 Aug 11;11(9):4960-4975. doi: 10.1364/BOE.397441. eCollection 2020 Sep 1.
Recently, we developed a label-free method to probe the microstructural information and optical properties of unstained thin tissue slices based on microscopic Mueller matrix imaging technique. In this paper, we take the microscopic Mueller matrix images of human breast ductal carcinoma tissue samples at different pathological stages, and then calculate and analyze their retardance-related Mueller matrix-derived parameters. To reveal the microstructural features more quantitatively and precisely, we propose a new method based on first-order statistical properties of image to transform the 2D images of Mueller matrix parameters into several statistical feature vectors. We evaluate each statistical feature vector by corresponding classification characteristic value extracted from the statistical features of Mueller matrix parameters images of healthy breast duct tissue samples. The experimental results indicate that these statistical feature vectors of Mueller matrix derived parameters may become powerful tools to quantitatively characterize breast ductal carcinoma tissue samples at different pathological stages. It has the potential to facilitate automating the staging process of breast ductal carcinoma tissue, resulting in the improvement of diagnostic efficiency.
最近，我们基于微观穆勒矩阵成像技术开发了一种无标记方法，用于探测未染色薄组织切片的微观结构信息和光学特性。在本文中，我们获取了不同病理阶段的人乳腺导管癌组织样本的微观穆勒矩阵图像，然后计算并分析了它们与延迟相关的穆勒矩阵衍生参数。为了更定量、精确地揭示微观结构特征，我们提出了一种基于图像一阶统计特性的新方法，将穆勒矩阵参数的二维图像转换为几个统计特征向量。我们通过从健康乳腺导管组织样本的穆勒矩阵参数图像的统计特征中提取的相应分类特征值来评估每个统计特征向量。实验结果表明，这些穆勒矩阵衍生参数的统计特征向量可能成为定量表征不同病理阶段乳腺导管癌组织样本的有力工具。它有可能促进乳腺导管癌组织分期过程的自动化，从而提高诊断效率。
