Synthetic and Systems Biology Unit, Biological Research Centre, Eötvös Loránd Research Network, Szeged, Hungary.
Doctoral School of Biology, University of Szeged, Szeged, Hungary.
Cell Rep Methods. 2022 Nov 21;2(12):100339. doi: 10.1016/j.crmeth.2022.100339. eCollection 2022 Dec 19.
Incorporating information about the surroundings can have a significant impact on successfully determining the class of an object. This is of particular interest when determining the phenotypes of cells, for example, in the context of high-throughput screens. We hypothesized that an ideal approach would consider the fully featured view of the cell of interest, include its neighboring microenvironment, and give lesser weight to cells that are far from the cell of interest. To satisfy these criteria, we present an approach with a transformation similar to those characteristic of fisheye cameras. Using this transformation with proper settings, we could significantly increase the accuracy of single-cell phenotyping, both in the case of cell culture and tissue-based microscopy images, and we present improved results on a dataset containing images of wild animals.
考虑周围环境的信息会对成功确定对象的类别产生重大影响。例如，在高通量筛选的情况下，确定细胞的表型时，这一点尤其有趣。我们假设理想的方法是考虑感兴趣的细胞的全特征视图，包括其邻近的微环境，并降低远离感兴趣细胞的细胞的权重。为了满足这些标准，我们提出了一种与鱼眼相机类似的变换方法。使用此变换和适当的设置，我们可以显著提高单细胞表型分析的准确性，无论是在细胞培养和基于组织的显微镜图像的情况下，并且我们在包含野生动物图像的数据集上呈现了改进的结果。
