USING MACHINE LEARNING TO SPEED UP MANUAL IMAGE ANNOTATION

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 背景图像分析是许多研究基因表达、细胞周期进程和蛋白质定位的生物学实验中的重要组成部分。一种追踪单个C. elegans的基因被开发出来，它可以通过3-D延时显微镜收集发育中胚胎的图像样本。在该协议中，一个名为StarryNite的程序执行荧光标记细胞的自动识别并跟踪它们的谱系。然而，由于数据中存在大量噪声，并且由于在开发的后期阶段增加细胞数量所带来的挑战，该程序并非没有错误。在当前版本中，错误校正（即，编辑）使用专门为此任务开发的名为AceTree的图形界面工具手动执行。对于单个实验，这种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。我们针对最常见的错误类型之一（注释为分割的运动），并训练SVM分类器来决定StarryNite进行的分割调用是否正确。通过对多个基准数据集的交叉验证实验，我们表明支持向量机成功地识别了此类错误。包括训练的SVM分类器的新版本StarryNite可在http://starrynite.sourceforge.net上获得。 结论：我们展示了机器学习方法在StarryNite错误注释中的实用性。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一个给定的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.