Abstract Mueller polarimetric imaging is considered a potentially powerful technique for probing the microstructural information in the biomedical field. In this study, the Mueller matrix microscopy was adopted to characterize the microstructures of breast cancer cells exhibiting different receptor proteins expressions. To be specific, four types of breast cancer cells were selected, and a suitable method was developed for cell sample preparation to capture clear cell polarimetric images. Subsequently, convolutional neural network was utilized to classify breast cancer cells with different input datasets types, and Mueller matrix elements images achieved the optimal accuracy of 88.3% (10.1% higher than that of ordinary optical images). The proposed technique demonstrated the potential application of Mueller polarimetric images to classify unstained cells harvested from breast cancer cytological biopsies. Furthermore, by immunofluorescence experiments and cytochalasin B treatment, this study verified that the polarization imaging can effectively show the intracellular localization and content of fibrous actin, which is critical to tumorigenesis and metastasis. It was thus indicated that Mueller matrix imaging can also help study the pathological process of breast cancer by displaying fibrous actin variations.

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基于Mueller偏振显微图像分析的乳腺癌细胞分类

摘要 Mueller 偏振成像被认为是一种潜在的强大技术，用于探测生物医学领域的微观结构信息。在这项研究中，采用穆勒矩阵显微镜来表征表现出不同受体蛋白表达的乳腺癌细胞的微观结构。具体而言，选择了四种类型的乳腺癌细胞，并开​​发了一种合适的细胞样品制备方法，以捕获清晰的细胞偏振图像。随后，利用卷积神经网络对不同输入数据集类型的乳腺癌细胞进行分类，Mueller矩阵元素图像达到了88.3%的最佳准确率（比普通光学图像高10.1%）。所提出的技术证明了穆勒偏振图像在对从乳腺癌细胞学活检中收获的未染色细胞进行分类的潜在应用。此外，通过免疫荧光实验和细胞松弛素 B 治疗，本研究证实偏振成像可以有效显示细胞内定位和纤维肌动蛋白的含量，这对肿瘤发生和转移至关重要。因此表明，Mueller 矩阵成像还可以通过显示纤维肌动蛋白的变化来帮助研究乳腺癌的病理过程。这对肿瘤发生和转移至关重要。因此表明，Mueller 矩阵成像还可以通过显示纤维肌动蛋白的变化来帮助研究乳腺癌的病理过程。这对肿瘤发生和转移至关重要。因此表明，Mueller 矩阵成像还可以通过显示纤维肌动蛋白的变化来帮助研究乳腺癌的病理过程。