Machine learning and computer vision approaches for phenotypic profiling.
Machine learning and computer vision approaches for phenotypic profiling.
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DOI：
10.1083/jcb.201610026
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发表时间：
2017-01-02
期刊：
影响因子：
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通讯作者：
Andrews BJ
中科院分区：
文献类型：
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作者：
Grys BT;Lo DS;Sahin N;Kraus OZ;Morris Q;Boone C;Andrews BJ
Grys等人回顾了已应用于基于图像的数据的表型分析的计算机视觉和机器学习方法。描述了分割，特征提取，选择和降维，以及聚类，离群值检测和数据分类。随着高通量自动化显微镜技术的最新进展，对有效的计算策略来分析大规模基于图像的数据的需求不断增加。为此，计算机视觉方法已被应用于细胞分割和特征提取，而机器学习方法已被开发，以帮助从生物图像获取的数据的表型分类和聚类。在这里，我们概述了用于生成和分类表型特征的常用计算机视觉和机器学习方法，突出了每种方法的一般生物学效用。
Grys et al. review computer vision and machine-learning methods that have been applied to phenotypic profiling of image-based data. Descriptions are provided for segmentation, feature extraction, selection, and dimensionality reduction, as well as clustering, outlier detection, and classification of data. With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach.