Kan Andrey
Division of Immunology, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia.
Immunol Cell Biol. 2017 Jul;95(6):525-530. doi: 10.1038/icb.2017.16. Epub 2017 Mar 15.
Machine learning (ML) refers to a set of automatic pattern recognition methods that have been successfully applied across various problem domains, including biomedical image analysis. This review focuses on ML applications for image analysis in light microscopy experiments with typical tasks of segmenting and tracking individual cells, and modelling of reconstructed lineage trees. After describing a typical image analysis pipeline and highlighting challenges of automatic analysis (for example, variability in cell morphology, tracking in presence of clutters) this review gives a brief historical outlook of ML, followed by basic concepts and definitions required for understanding examples. This article then presents several example applications at various image processing stages, including the use of supervised learning methods for improving cell segmentation, and the application of active learning for tracking. The review concludes with remarks on parameter setting and future directions.
机器学习（ML）是指一组自动模式识别方法，已成功应用于包括生物医学图像分析在内的各种问题领域。本综述重点关注机器学习在光学显微镜实验图像分析中的应用，这些实验的典型任务包括分割和跟踪单个细胞，以及对重建的谱系树进行建模。在描述了典型的图像分析流程并强调自动分析的挑战（例如，细胞形态的变异性、存在杂波时的跟踪）之后，本综述简要回顾了机器学习的发展历程，接着介绍理解示例所需的基本概念和定义。本文随后展示了在各个图像处理阶段的几个示例应用，包括使用监督学习方法改进细胞分割，以及应用主动学习进行跟踪。综述最后对参数设置和未来方向进行了讨论。
