Graduate Program of Genetics, Genomics and Bioinformatics, University of California, Riverside, Riverside, CA, 92521, USA.
School of Medicine, Division of Biomedical Sciences, University of California, Riverside, Riverside, CA, 92521, USA.
Sci Rep. 2023 May 22;13(1):8213. doi: 10.1038/s41598-023-34943-w.
Counting cells is a cornerstone of tracking disease progression in neuroscience. A common approach for this process is having trained researchers individually select and count cells within an image, which is not only difficult to standardize but also very time-consuming. While tools exist to automatically count cells in images, the accuracy and accessibility of such tools can be improved. Thus, we introduce a novel tool ACCT: Automatic Cell Counting with Trainable Weka Segmentation which allows for flexible automatic cell counting via object segmentation after user-driven training. ACCT is demonstrated with a comparative analysis of publicly available images of neurons and an in-house dataset of immunofluorescence-stained microglia cells. For comparison, both datasets were manually counted to demonstrate the applicability of ACCT as an accessible means to automatically quantify cells in a precise manner without the need for computing clusters or advanced data preparation.
细胞计数是神经科学中跟踪疾病进展的基础。一种常见的方法是让经过训练的研究人员单独选择并计数图像中的细胞，这种方法不仅难以标准化，而且非常耗时。虽然有工具可以自动计数图像中的细胞，但这些工具的准确性和可访问性可以进一步提高。因此，我们引入了一种新的工具 ACCT：使用可训练的 WEKA 分割进行自动细胞计数，该工具允许在用户驱动的培训后通过对象分割进行灵活的自动细胞计数。ACCT 通过对神经元的公开图像和免疫荧光染色的小胶质细胞的内部数据集进行比较分析来进行演示。为了进行比较，两个数据集都进行了手动计数，以证明 ACCT 是一种可访问的方法，可以自动以精确的方式定量细胞，而无需计算聚类或进行高级数据准备。
