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用于清理非纤毛污染的纤毛Q输出的删除感兴趣区域(DeleteROI)。

DeleteROI for Cleaning CiliaQ Output of Non-ciliary Contamination.

作者信息

Anuszczyk Jeffrey J, Stuck Michael W, Eguether Thibaut, Pazour Gregory J

机构信息

Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, United States.

Inserm UMS55 ART ARNm, Inserm UMS55 ART ARNm, LI2RSO, Université d'Orléans, CHU d'Orléans, 45100 Orléans, France.

出版信息

MicroPubl Biol. 2025 Aug 14;2025. doi: 10.17912/micropub.biology.001670. eCollection 2025.

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.

摘要

汉森及其同事开发的ImageJ插件CiliaQ(Hansen等人,2021年)可对三维空间中的纤毛参数进行复杂分析。然而,中间体和其他非纤毛结构可能会污染输出结果,并且需要付出巨大努力才能去除。此外,由于数据对研究者不设盲,手动去除污染物存在主观偏差的风险。为了解决这些问题,我们开发了一个ImageJ插件,该插件以可点击网格的形式呈现由CiliaQ识别出的纤毛感兴趣区域(ROI)的图像,从而能够标记并自动去除非纤毛污染物。为了减少主观偏差,我们的插件处理多个图像的数据集,并随机呈现纤毛ROI。如果数据集中同时包含对照和实验条件,纤毛会随机散布,且没有关于其实验组的可见信息,从而减少主观偏差。去除污染物后,输出清理后的数据,并保持最初使用的CiliaQ文件格式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bc22/12395279/a1500b3f274a/25789430-2025-micropub.biology.001670.jpg

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