Department of Computational Biology, Center for Advanced Studies, St.Petersburg State Polytechnical University, St.Petersburg, 195251, Russia.
BMC Bioinformatics. 2011 Aug 4;12:320. doi: 10.1186/1471-2105-12-320.
Accuracy of the data extracted from two-dimensional confocal images is limited due to experimental errors that arise in course of confocal scanning. The common way to reduce the noise in images is sequential scanning of the same specimen several times with the subsequent averaging of multiple frames. Attempts to increase the dynamical range of an image by setting too high values of microscope PMT parameters may cause clipping of single frames and introduce errors into the data extracted from the averaged images. For the estimation and correction of this kind of errors a method based on censoring technique (Myasnikova et al., 2009) is used. However, the method requires the availability of all the confocal scans along with the averaged image, which is normally not provided by the standard scanning procedure.
To predict error size in the data extracted from the averaged image we developed a regression system. The system is trained on the learning sample composed of images obtained from three different microscopes at different combinations of PMT parameters, and for each image all the scans are saved. The system demonstrates high prediction accuracy and was applied for correction of errors in the data on segmentation gene expression in Drosophila blastoderm stored in the FlyEx database (http://urchin.spbcas.ru/flyex/, http://flyex.uchicago.edu/flyex/). The prediction method is realized as a software tool CorrectPattern freely available at http://urchin.spbcas.ru/asp/2011/emm/.
We created a regression system and software to predict the magnitude of errors in the data obtained from a confocal image based on information about microscope parameters used for the image acquisition. An important advantage of the developed prediction system is the possibility to accurately correct the errors in data obtained from strongly clipped images, thereby allowing to obtain images of the higher dynamical range and thus to extract more detailed quantitative information from them.
由于共聚焦扫描过程中出现的实验误差，从二维共焦图像中提取的数据的准确性受到限制。减少图像噪声的常用方法是对同一标本进行多次顺序扫描，然后对多个帧进行平均。通过设置过高的显微镜 PMT 参数值来尝试增加图像的动态范围，可能会导致单个帧被裁剪，并在从平均图像中提取的数据中引入误差。为了估计和纠正这种误差，使用了基于屏蔽技术（Myasnikova 等人，2009）的方法。然而，该方法需要具有所有共聚焦扫描以及平均图像，而标准扫描程序通常不提供平均图像。
为了预测从平均图像中提取的数据中的误差大小，我们开发了一个回归系统。该系统在由从三个不同显微镜在不同 PMT 参数组合下获得的图像组成的学习样本上进行训练，并且为每个图像都保存了所有的扫描。该系统表现出很高的预测准确性，并应用于纠正存储在 FlyEx 数据库中的果蝇胚胎分割基因表达数据中的误差（http://urchin.spbcas.ru/flyex/，http://flyex.uchicago.edu/flyex/）。预测方法实现为一个软件工具 CorrectPattern，可在 http://urchin.spbcas.ru/asp/2011/emm/ 上免费获得。
我们创建了一个回归系统和软件，以根据用于获取图像的显微镜参数的信息，预测从共焦图像获得的数据中的误差大小。开发的预测系统的一个重要优点是能够准确纠正从强烈裁剪的图像中获得的数据中的误差，从而可以获得更高动态范围的图像，并从中提取更详细的定量信息。
