USING MACHINE LEARNING TO SPEED UP MANUAL IMAGE ANNOTATION

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 背景图像分析是研究基因表达、细胞周期进程和蛋白质定位的许多生物学实验的重要组成部分。开发了一种追踪线虫单个基因表达的方法，该方法通过三维时间推移显微镜收集发育中的胚胎图像样本。在该协议中，一个名为StarryNite的程序执行对荧光标记细胞的自动识别并追踪它们的谱系。然而，由于数据中存在的大量噪声，以及由于在开发的后期阶段增加单元数量所带来的挑战，该程序并非没有错误。在当前版本中，纠错(即编辑)是使用专门为这项任务开发的名为AceTree的图形界面工具手动执行的。对于单个实验，这项手动注释任务需要几个小时。 结果：在本文中，我们减少了纠正StarryNite错误所需的时间。我们针对最频繁的错误类型之一(标记为分区的移动)训练支持向量机分类器，以确定StarryNite进行的分区调用是否正确。通过在几个基准数据集上的交叉验证实验，我们证明了支持向量机能够显著地识别这种类型的错误。新版StarryNite包括训练好的支持向量机分类器，可在http://starrynite.sourceforge.net.上获得 结论：我们演示了机器学习方法在StarryNite错误标注中的实用性。在这个过程中，我们还提供了一些通用的方法来开发和验证关于给定模式识别任务的分类器。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Background Image analysis is an essential component in many biological experiments that study gene expression, cell cycle progression, and protein localization. A protocol for tracking the expression of individual C. elegans genes was developed that collects image samples of a developing embryo by 3-D time lapse microscopy. In this protocol, a program called StarryNite performs the automatic recognition of fluorescently labeled cells and traces their lineage. However, due to the amount of noise present in the data and due to the challenges introduced by increasing number of cells in later stages of development, this program is not error free. In the current version, the error correction (i.e., editing) is performed manually using a graphical interface tool named AceTree, which is specifically developed for this task. For a single experiment, this manual annotation task takes several hours. Results: In this paper, we reduce the time required to correct errors made by StarryNite. We target one of the most frequent error types (movements annotated as divisions) and train an SVM classifier to decide whether a division call made by StarryNite is correct or not. We show, via cross-validation experiments on several benchmark data sets, that the SVM successfully identifies this type of error significantly. A new version of StarryNite that includes the trained SVM classifier is available at http://starrynite.sourceforge.net. Conclusions: We demonstrate the utility of a machine learning approach to error annotation for StarryNite. In the process, we also provide some general methodologies for developing and validating a classifier with respect to a given pattern recognition task.