In cell-based research, the process of visually monitoring cells generates large image datasets that need to be evaluated for quantifiable information in order to track the effectiveness of treatments in vitro. With the traditional, end-point assay-based approach being error-prone, and existing computational approaches being complex, we tested existing machine learning frameworks to find methods that are relatively simple, yet powerful enough to accomplish the goal of analyzing cell microscopy data. This paper details the machine learning pipeline for pixel-based classification and object-based classification. Furthermore, it compares the performances of three classifiers. The classifiers evaluated were the fast-random forest (RF), the sequential minimal optimization (SMO), and the Bayesian network (BN). Images were first preprocessed using smoothing and contrast methods found in FIJI. For pixel-based classification, the preprocessed images were fed into the Trainable Waikato Segmentation (TWS). For object-based classification, training and classification were conducted within the Waikato Environment for Knowledge Analysis (WEKA) interface. All classifiers' performance was evaluated using the WEKA experimental explorer. In terms of performance, the BN had the lowest classification accuracy for both the pixel-based and object-based model. The object-based SMO classifier had the best performance with the lowest mean absolute error of 0.05. The TWS and WEKA interface allows users to easily create and train classifiers for image analysis. However, for analyzing large image datasets, they are not ideal. Grapical abstract.

中文翻译：

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对LN-18胶质细胞形态的变化进行分类：一种通过FIJI和WEKA分析细胞显微镜数据的有监督的机器学习方法。

在基于细胞的研究中，以视觉方式监视细胞的过程会生成大图像数据集，需要对其进行量化信息评估，以便跟踪体外治疗的有效性。由于传统的基于终点分析的方法容易出错，而现有的计算方法却很复杂，因此我们测试了现有的机器学习框架，以找到相对简单但功能强大的方法来完成分析细胞显微镜数据的目的。本文详细介绍了基于像素分类和基于对象分类的机器学习管道。此外，它比较了三个分类器的性能。评估的分类器是快速随机森林（RF），顺序最小优化（SMO）和贝叶斯网络（BN）。首先使用FIJI中的平滑和对比方法对图像进行预处理。对于基于像素的分类，将预处理后的图像输入到可训练的怀卡托分割（TWS）中。对于基于对象的分类，在怀卡托知识分析环境（WEKA）界面内进行了培训和分类。使用WEKA实验浏览器评估了所有分类器的性能。在性能方面，对于基于像素和基于对象的模型，BN的分类精度最低。基于对象的SMO分类器具有最佳性能，平均绝对误差最低，为0.05。TWS和WEKA界面使用户可以轻松创建和训练分类器以进行图像分析。但是，对于分析大型图像数据集，它们并不理想。语法摘要。