Leibniz Institute of Photonic Technology, Albert-Einstein-Straße 9, D-07745 Jena, Germany.
Lab Chip. 2020 May 5;20(9):1676-1686. doi: 10.1039/d0lc00244e.
Multidirectional imaging flow cytometry (mIFC) extends conventional imaging flow cytometry (IFC) for the image-based measurement of 3D-geometrical features of particles. The innovative core is a flow rotation unit in which a vertical sample lamella is incrementally rotated by 90 degrees into a horizontal lamella. The required multidirectional views are generated by guiding all particles at a controllable shear flow position of the parabolic velocity profile of the capillary slit detection chamber. All particles pass the detection chamber in a two-dimensional sheet under controlled rotation while each particle is imaged multiple times. This generates new options for automated particle analysis. In an experimental application, we used our system for the accurate classification of 15 species of pollen based on 3D-morphological information. We demonstrate how the combination of multi directional imaging with advanced machine learning algorithms can improve the accuracy of automated bio-particle classification. As an additional benefit, we significantly decrease the number of false positives in the classification of foreign particles, i.e. those elements which do not belong to one of the trained classes by the 3D-extension of the classification algorithm.
多维成像流式细胞术（mIFC）扩展了传统的成像流式细胞术（IFC），可基于图像测量颗粒的 3D 几何特征。创新的核心是一个流动旋转单元，其中垂直的样本薄片通过 90 度逐步旋转成水平薄片。所需的多维视图是通过在毛细管狭缝检测室的抛物线速度分布的可控剪切流位置引导所有颗粒来生成的。所有颗粒在受控旋转下以二维薄片形式通过检测室，而每个颗粒被多次成像。这为自动化颗粒分析提供了新的选择。在一个实验应用中，我们使用我们的系统基于 3D 形态学信息对 15 种花粉进行了准确分类。我们展示了多方向成像与先进的机器学习算法相结合如何提高自动生物粒子分类的准确性。作为一个额外的好处，我们通过分类算法的 3D 扩展，大大减少了分类中外来颗粒（即不属于训练类别之一的元素）的假阳性数量。
