ilastik: interactive machine learning for (bio) image analysis
ilastik: interactive machine learning for (bio) image analysis
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DOI：
10.1038/s41592-019-0582-9
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发表时间：
2019-12-01
期刊：
影响因子：
48
通讯作者：
Kreshuk, Anna
中科院分区：
文献类型：
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作者：
Berg, Stuart;Kutra, Dominik;Kreshuk, Anna
我们介绍ilastik，一个易于使用的交互式工具，为终端用户提供基于机器学习的（生物）图像分析，而无需大量的计算专业知识。它包含预定义的工作流程，用于图像分割，对象分类，计数和跟踪。用户通过交互式地为非线性分类器提供稀疏训练注释，使工作流适应手头的问题。ilastik可以处理多达五个维度的数据（3D，时间和通道数量）。它的计算后端尽可能按需运行操作，允许对大于RAM的数据进行交互式预测。一旦分类器得到训练，ilastik工作流就可以从命令行应用于新数据，而无需进一步的用户交互。我们详细描述了所有的ilastik工作流程，包括三个案例研究和对预期性能的讨论。
We present ilastik, an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise. It contains pre-defined workflows for image segmentation, object classification, counting and tracking. Users adapt the workflows to the problem at hand by interactively providing sparse training annotations for a non-linear classifier. ilastik can process data in up to five dimensions (3D, time and number of channels). Its computational back end runs operations on-demand wherever possible, allowing for interactive prediction on data larger than RAM. Once the classifiers are trained, ilastik workflows can be applied to new data from the command line without further user interaction. We describe all ilastik workflows in detail, including three case studies and a discussion on the expected performance.