计算机科学
工作流程
机器学习
人工智能
分类器(UML)
分割
图像(数学)
模式识别(心理学)
数据挖掘
数据库
作者
Stuart Berg,Dominik Kutra,Thorben Kroeger,Christoph Straehle,Bernhard X. Kausler,Carsten Haubold,Martin Schiegg,Janez Aleš,Thorsten Beier,Markus Rudy,Kemal Eren,Jaime I Cervantes,Buote Xu,Fynn Beuttenmueller,Adrian Wolny,Chong Zhang,Ullrich Koethe,Fred A. Hamprecht,Anna Kreshuk
出处
期刊:Nature Methods
[Springer Nature]
日期:2019-09-30
卷期号:16 (12): 1226-1232
被引量:2320
标识
DOI:10.1038/s41592-019-0582-9
摘要
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 nonlinear 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. ilastik is an user-friendly interactive tool for machine-learning-based image segmentation, object classification, counting and tracking.
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