Quantitative phase imaging has gained popularity in bioimaging because it can avoid the need for cell staining, which, in some cases, is difficult or impossible. However, as a result, quantitative phase imaging does not provide the labelling of various specific intracellular structures. Here we show a novel computational segmentation method based on statistical inference that makes it possible for quantitative phase imaging techniques to identify the cell nucleus. We demonstrate the approach with refractive index tomograms of stain-free cells reconstructed using tomographic phase microscopy in the flow cytometry mode. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal fluorescence microscopy data and microfluidic cyto-fluorimeter outputs. This is a remarkable step towards directly extracting specific three-dimensional intracellular structures from the phase contrast data in a typical flow cytometry configuration.

定量相位成像在生物成像中越来越受欢迎，因为它可以避免细胞染色的需要，而细胞染色在某些情况下是困难或不可能的。然而，定量相位成像不能提供各种特定细胞内结构的标记。在这里，我们展示了一种基于统计推断的新型计算分割方法，该方法使得定量相位成像技术能够识别细胞核。我们通过在流式细胞术模式下使用断层相显微镜重建的无染色细胞的折射率断层图来演示该方法。特别是，通过数值模拟和两种癌细胞系，我们证明可以在免染色断层扫描中准确地区分细胞核。我们表明，我们的实验结果与共焦荧光显微镜数据和微流控细胞荧光计输出一致。这是在典型流式细胞仪配置中从相差数据中直接提取特定三维细胞内结构的显着一步。