Institute of Microbiology, ETH Zurich, Zürich, Switzerland.
BMC Bioinformatics. 2010 Jan 14;11:30. doi: 10.1186/1471-2105-11-30.
Light microscopy is of central importance in cell biology. The recent introduction of automated high content screening has expanded this technology towards automation of experiments and performing large scale perturbation assays. Nevertheless, evaluation of microscopy data continues to be a bottleneck in many projects. Currently, among open source software, CellProfiler and its extension Analyst are widely used in automated image processing. Even though revolutionizing image analysis in current biology, some routine and many advanced tasks are either not supported or require programming skills of the researcher. This represents a significant obstacle in many biology laboratories.
We have developed a tool, Enhanced CellClassifier, which circumvents this obstacle. Enhanced CellClassifier starts from images analyzed by CellProfiler, and allows multi-class classification using a Support Vector Machine algorithm. Training of objects can be done by clicking directly "on the microscopy image" in several intuitive training modes. Many routine tasks like out-of focus exclusion and well summary are also supported. Classification results can be integrated with other object measurements including inter-object relationships. This makes a detailed interpretation of the image possible, allowing the differentiation of many complex phenotypes. For the generation of the output, image, well and plate data are dynamically extracted and summarized. The output can be generated as graphs, Excel-files, images with projections of the final analysis and exported as variables.
Here we describe Enhanced CellClassifier which allows multiple class classification, elucidating complex phenotypes. Our tool is designed for the biologist who wants both, simple and flexible analysis of images without requiring programming skills. This should facilitate the implementation of automated high-content screening.
显微镜在细胞生物学中具有核心地位。最近引入的自动化高通量筛选技术已经将该技术扩展到了实验自动化和进行大规模扰动测定。然而，显微镜数据的评估仍然是许多项目的瓶颈。目前，在开源软件中，CellProfiler 及其扩展版 Analyst 被广泛应用于自动化图像处理。尽管在当前生物学中彻底改变了图像分析，但一些常规和许多高级任务既不受支持，也需要研究人员具备编程技能。这在许多生物学实验室中构成了一个重大障碍。
我们开发了一种工具，称为 enhanced CellClassifier，它可以克服这一障碍。enhanced CellClassifier 从 CellProfiler 分析的图像开始，允许使用支持向量机算法进行多类分类。可以通过在几种直观的训练模式下直接在“显微镜图像”上点击来训练对象。许多常规任务，如离焦排除和孔板总结也得到了支持。分类结果可以与其他对象测量值（包括对象之间的关系）集成。这使得对图像进行详细解释成为可能，可以区分许多复杂的表型。为了生成输出，动态提取和汇总图像、孔板和板数据。输出可以生成图形、Excel 文件、带有最终分析投影的图像，并作为变量导出。
在这里，我们描述了 enhanced CellClassifier，它允许进行多类分类，阐明复杂的表型。我们的工具专为希望进行简单灵活的图像分析而又不需要编程技能的生物学家而设计。这应该有助于实现自动化高通量筛选。
