Jeffrey J Anuszczyk, Michael W Stuck, Thibaut Eguether, Gregory J Pazour
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引用次数: 0
Abstract
The ImageJ plugin CiliaQ developed by Hansen and colleagues (Hansen et al., 2021) provides for sophisticated analysis of ciliary parameters in three-dimensional space. However, midbodies and other non-ciliary structures can contaminate the output and require significant effort to remove. Furthermore, the manual removal of contamination risks subjective bias as the data is not blinded to the investigator. To address these problems, we developed an ImageJ plugin that presents images of the cilia region-of-interests (ROIs) identified by CiliaQ in a clickable grid that allows for marking and automated removal of non-ciliary contaminants. To reduce subjective bias, our plugin works on a dataset of multiple images and presents the cilia ROIs randomly. If the dataset contains both control and experimental conditions, the cilia are randomly interspersed with no visible information about their experimental group, thus reducing subjective bias. After removal of contamination, the cleaned data is output maintaining the CiliaQ file formats initially used.
Hansen及其同事(Hansen et al., 2021)开发的ImageJ插件CiliaQ提供了三维空间中纤毛参数的复杂分析。然而,中间体和其他非纤毛结构可能会污染输出,需要付出很大的努力才能清除。此外,人工去除污染的风险主观偏见,因为数据不是盲目的研究者。为了解决这些问题,我们开发了一个ImageJ插件,该插件将CiliaQ识别的纤毛兴趣区域(roi)图像呈现在可点击的网格中,允许标记和自动去除非纤毛污染物。为了减少主观偏见,我们的插件在多个图像的数据集上工作,并随机呈现纤毛的roi。如果数据集同时包含对照和实验条件,纤毛是随机分布的,没有关于实验组的可见信息,从而减少了主观偏差。清除污染后,输出清理后的数据,并保持最初使用的CiliaQ文件格式。