Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Bioinformatics. 2021 Nov 5;37(21):3992-3994. doi: 10.1093/bioinformatics/btab634.
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.
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.
基于图像的实验可以产生数千个单独的测量值，用于描述每个感兴趣的对象，例如显微镜屏幕中的细胞。CellProfiler Analyst 是一个免费的开源软件包，专为探索定量图像衍生数据和使用直观的用户界面训练机器学习分类器而设计。我们现在发布了 CellProfiler Analyst 3.0，除了增强性能外，它还支持神经网络分类器、识别罕见对象子集以及直接将感兴趣的对象从可视化工具传输到 Classifier 工具中作为训练数据使用。此版本还增加了与最近发布的 CellProfiler 4 的互操作性，使用户更容易在分析中检测和测量特定类别的对象。
可在 https://cellprofileranalyst.org/ 免费下载适用于 Windows 和 MacOS 的 CellProfiler Analyst 二进制文件。源代码是用 Python 3 实现的，并可在 https://github.com/CellProfiler/CellProfiler-Analyst/ 上获得。一个示例数据集可在 https://cellprofileranalyst.org/examples 上获得，该数据集基于 Broad Bioimage Benchmark Collection 中免费提供的图像。
