We present a method for real‐time visualization and automatic processing for detection and classification of untreated 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 the blood, following an initial label‐free rapid enrichment stage based on the cell size, we applied our holographic imaging approach, providing the quantitative optical thickness profiles of the cells during flow. 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 image cells during flow quantitatively. 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% for distinguishing between the cells, enabling further automatic enrichment and cancer‐cell grading from blood. © 2020 International Society for Advancement of Cytometry

中文翻译：

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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

我们提出了一种实时可视化和自动处理的方法，用于在无染色成像流式细胞术期间检测和分类血液中未处理的癌细胞，使用数字全息显微镜和机器学习，吞吐量为每秒 15 个细胞。作为血液中循环肿瘤细胞的初步模型，在基于细胞大小的初始无标记快速富集阶段之后，我们应用了我们的全息成像方法，提供了流动过程中细胞的定量光学厚度分布。我们自动对原发性和转移性结肠癌细胞进行分类，其中两种癌细胞是从同一个体中分离出来的，以及四种血细胞。我们使用低相干离轴干涉相位显微镜和微流体通道对流动过程中的细胞进行定量成像。获得的图像根据细胞流动过程中的形态和定量相位特征进行处理和分类。我们在区分细胞方面达到了 92.56% 的高精度，从而能够进一步从血液中自动富集和癌细胞分级。© 2020 国际细胞计量学促进会