使用干涉相位显微镜和机器学习对癌细胞和血细胞进行实时无染色分类
Real-time stain-free classification of cancer cells and blood cells using interferometric phase microscopy and machine learning
摘要：
我们提出了一种实时可视化和自动处理的方法，用于在免染色成像流式细胞术期间使用数字全息显微镜和机器学习以每秒15个细胞的吞吐量检测和分类血液中未接触的癌细胞。作为血液中循环肿瘤细胞的初步模型，我们自动分类了原发性和转移性结肠癌细胞，其中两种类型的癌细胞分离自同一个体，以及四种类型的血细胞。我们使用低相干离轴干涉相位显微镜和微流体通道，以定量成像细胞在流动过程中。采集的图像进行处理和分类的基础上，它们的形态和定量相特征的细胞流动。我们在区分细胞方面达到了92.56%的高准确率，为未来血液样本中的自动无标记癌细胞分类铺平了道路。
英文摘要：
We present a method for a real time visualization and automatic processing for detection and classification of untouched cancer cells in blood during stain free imaging flow cytometry using digital holographic microscopy and machine learning in throughput of 15 cells per second. As a preliminary model for circulating tumor cells in blood, we automatically classified primary and metastatic colon cancer cells, where the two types of cancer cells were isolated from the same individual, as well as four types of blood cells. We used low-coherence off-axis interferometric phase microscopy and a microfluidic channel to quantitatively image cells during flow. The acquired images were processed and classified based on their morphology and quantitative phase features during the cell flow. We achieved high accuracy of 92.56 percent for distinguishing between the cells, paving the way for future automatic label-free cancer cell classification in blood samples.
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