Enhanced CellClassifier: a multi-class classification tool for microscopy images.
Enhanced CellClassifier: a multi-class classification tool for microscopy images.
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DOI：
10.1186/1471-2105-11-30
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发表时间：
2010-01-14
影响因子：
3
通讯作者：
Hardt WD
中科院分区：
文献类型：
--
作者：
Misselwitz B;Strittmatter G;Periaswamy B;Schlumberger MC;Rout S;Horvath P;Kozak K;Hardt WD
光学显微镜在细胞生物学中至关重要。最近引入的自动化高内涵筛选已将该技术扩展到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，开源软件中，CellProfiler及其扩展Analyst被广泛应用于自动化图像处理。尽管彻底改变了当前生物学中的图像分析，但一些常规和许多高级任务要么不受支持，要么需要研究人员的编程技能。这对许多生物实验室来说是一个重大障碍。我们开发了一种工具，Enhanced CellClassifier，可以绕过这个障碍。增强型 CellClassifier 从 CellProfiler 分析的图像开始，并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单击“显微镜图像”来完成对象的训练。还支持许多常规任务，例如失焦排除和井总结。分类结果可以与其他对象测量（包括对象间关系）集成。这使得对图像的详细解释成为可能，从而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、孔和板数据。输出可以生成图表、Excel 文件、带有最终分析投影的图像，并导出为变量。在这里，我们描述了增强型细胞分类器，它允许进行多类分类，阐明复杂的表型。我们的工具是为那些想要简单而灵活地分析图像而无需编程技能的生物学家而设计的。这应该有助于自动化高内涵筛选的实施。
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.
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影响因子：
12.3
作者：
Boutros M;Brás LP;Huber W
通讯作者：
Huber W
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
48
作者：
Loo, Lit-Hsin;Wu, Lani F.;Altschuler, Steven J.
通讯作者：
Altschuler, Steven J.
DOI：
10.1109/titb.2007.913128
发表时间：
2008-07-01
影响因子：
--
作者：
Huang, Chun-Rong;Chung, Pau-Choo;Popper, Mikulas
通讯作者：
Popper, Mikulas
影响因子：
64.5
作者：
Palamidessi, Andrea;Frittoli, Emanuela;Di Fiore, Pier Paolo
通讯作者：
Di Fiore, Pier Paolo