Summary Image-based experiments can yield many thousands of individual measurements describing each object of interest, such as cells in microscopy screens. CellProfiler Analyst is a free, open-source software package designed for the exploration of quantitative image-derived data and the training of machine learning classifiers with an intuitive user interface. We have now released CellProfiler Analyst 3.0, which in addition to enhanced performance adds support for neural network classifiers, identifying rare object subsets, and direct transfer of objects of interest from visualization tools into the Classifier tool for use as training data. This release also increases interoperability with the recently released CellProfiler 4, making it easier for users to detect and measure particular classes of objects in their analyses. Availability CellProfiler Analyst binaries for Windows and MacOS are freely available for download at https://cellprofileranalyst.org/. Source code is implemented in Python 3 and is available at https://github.com/CellProfiler/CellProfiler-Analyst/. A sample dataset is available at https://cellprofileranalyst.org/examples, based on images freely available from the Broad Bioimage Benchmark Collection.

中文翻译：

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CellProfiler Analyst 3.0：用于图像分析的可访问数据探索和机器学习

总结 基于图像的实验可以产生数千个描述每个感兴趣对象的单独测量值，例如显微镜屏幕中的细胞。CellProfiler Analyst 是一个免费的开源软件包，设计用于探索定量图像衍生数据和训练机器学习分类器，具有直观的用户界面。我们现在发布了 CellProfiler Analyst 3.0，除了增强的性能外，它还增加了对神经网络分类器、识别稀有对象子集以及将感兴趣的对象从可视化工具直接传输到分类器工具以用作训练数据的支持。此版本还增加了与最近发布的 CellProfiler 4 的互操作性，使用户更容易在他们的分析中检测和测量特定类别的对象。适用于 Windows 和 MacOS 的 CellProfiler Analyst 二进制文件可从 https://cellprofileranalyst.org/ 免费下载。源代码在 Python 3 中实现，可从 https://github.com/CellProfiler/CellProfiler-Analyst/ 获取。示例数据集可在 https://cellprofileranalyst.org/examples 上获得，该数据集基于可从 Broad Bioimage Benchmark Collection 免费获得的图像。