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New software for automated cilia detection in cells (ACDC)

机译：用于细胞中纤毛自动检测的新软件（ACDC）

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Primary cilia frequency and length are key metrics in studies of ciliogenesis and ciliopathies. Typically, quantitative cilia analysis is done manually, which is very time-consuming. While some open-source and commercial image analysis software applications can segment input data, they still require the user to optimize many parameters, suffer from user bias, and often lack rigorous performance quality assessment (e.g., false positives and false negatives). Further, optimal parameter combinations vary in detection accuracy depending on cilia reporter, cell type, and imaging modality. A good automated solution would analyze images quickly, robustly, and adaptably—across different experimental data sets—without significantly compromising the accuracy of manual analysis. To solve this problem, we developed a new software for automated cilia detection in cells (ACDC). The software operates through four main steps: image importation, pre-processing, detection auto-optimization, and analysis. From a data set, a representative image with manually selected cilia (i.e., Ground Truth) is used for detection auto-optimization based on four parameters: signal-to-noise ratio, length, directional score, and intensity standard deviation. Millions of parameter combinations are automatically evaluated and optimized according to an accuracy ‘F1’ score, based on the amount of false positives and false negatives. Afterwards, the optimized parameter combination is used for automated detection and analysis of the entire data set. The ACDC software accurately and adaptably detected nuclei and primary cilia across different cell types (NIH3T3, RPE1), cilia reporters (AcTub, Smo-GFP, Arl13b), and image magnifications (60×, 40×). We found that false-positive and false-negative rates for Arl13b-stained cilia were 1–6%, yielding high F1 scores of 0.96–0.97 (max.?=?1.00). The software detected significant differences in mean cilia length between control and cytochalasin D-treated cell populations and could monitor dynamic changes in cilia length from movie recordings. Automated analysis offered up to a 96-fold speed enhancement compared to manual analysis, requiring around 5?s/image, or nearly 18,000 cilia analyzed/hour. The ACDC software is a solution for robust automated analysis of microscopic images of ciliated cells. The software is extremely adaptable, accurate, and offers immense time-savings compared to traditional manual analysis.
机译：初级纤毛的频率和长度是纤毛发生和纤毛病研究的关键指标。通常，定量纤毛分析是手动完成的，这非常耗时。尽管某些开源和商业图像分析软件应用程序可以分割输入数据，但它们仍然需要用户优化许多参数，遭受用户偏见并常常缺乏严格的性能质量评估（例如，误报和误报）。此外，最佳参数组合的检测精度取决于纤毛报告基因，细胞类型和成像方式而有所不同。一个好的自动化解决方案可以跨不同的实验数据集快速，稳健和适应性地分析图像，而不会显着影响手动分析的准确性。为了解决这个问题，我们开发了一种用于细胞中纤毛自动检测的新软件（ACDC）。该软件通过四个主要步骤进行操作：图像导入，预处理，检测自动优化和分析。从数据集中，具有手动选择的纤毛（即地面真相）的代表性图像用于基于四个参数进行检测自动优化：信噪比，长度，方向得分和强度标准偏差。数以百万计的参数组合会根据误报和误报的数量，根据准确性“ F1”分数自动进行评估和优化。然后，将优化的参数组合用于自动检测和分析整个数据集。 ACDC软件可准确，自适应地检测不同细胞类型（NIH3T3，RPE1），纤毛报告基因（AcTub，Smo-GFP，Arl13b）和图像放大倍率（60倍，40倍）的细胞核和初级纤毛。我们发现，Arl13b染色的纤毛的假阳性和假阴性率为1–6％，F1得分较高，为0.96–0.97（最大？=？1.00）。该软件检测到对照组和经细胞松弛素D处理的细胞群之间的平均纤毛长度存在显着差异，并且可以通过电影录制监视纤毛长度的动态变化。与手动分析相比，自动分析的速度提高了96倍，需要大约5？s /图像，或每小时将近18,000纤毛被分析。 ACDC软件是用于对纤毛细胞显微图像进行强大的自动化分析的解决方案。与传统的手动分析相比，该软件具有极强的适应性，准确性，并且可以节省大量时间。

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