Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich, Germany.
J Neurosci Methods. 2019 Oct 1;326:108394. doi: 10.1016/j.jneumeth.2019.108394. Epub 2019 Aug 12.
Cell counting in neuroscience is a routine method of utmost importance to support descriptive in vivo findings with quantitative data on the cellular level. Although known to be error- and bias-prone, manual cell counting of histological stained brain slices remains the gold standard in the field. While the manual approach is limited to small regions-of-interest in the brain, automated tools are needed to up-scale translational approaches and generate whole mouse brain counts in an atlas framework. Our goal was to develop an algorithm which requires no pre-training such as machine learning algorithms, only minimal user input, and adjustable variables to obtain reliable cell counting results for stitched mouse brain slices registered to a common atlas such as the Allen Mouse Brain atlas. We adapted filter banks to extract the maxima from round-shaped cell nuclei and various cell structures. In a qualitative as well as quantitative comparison to other tools and two expert raters, AIDAhisto provides accurate and fast results for cell nuclei as well as immunohistochemical stainings of various types of cells in the mouse brain.
神经科学中的细胞计数是一种非常重要的常规方法，它可以用细胞水平的定量数据来支持描述性的体内发现。尽管众所周知，手动细胞计数存在误差和偏差，但在该领域，组织学染色脑切片的手动细胞计数仍然是金标准。虽然手动方法仅限于大脑的小感兴趣区域，但需要自动化工具来扩大转化方法，并在图谱框架中生成整个小鼠大脑的计数。我们的目标是开发一种算法，该算法不需要像机器学习算法那样的预训练，只需要最少的用户输入和可调整的变量，就可以为注册到通用图谱（如 Allen Mouse Brain 图谱）的拼接小鼠脑切片获得可靠的细胞计数结果。我们适应滤波器组从圆形细胞核和各种细胞结构中提取最大值。与其他工具和两位专家评分者的定性和定量比较，AIDAhisto 为小鼠大脑中的细胞核以及各种类型的细胞的免疫组织化学染色提供了准确和快速的结果。
