Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification
Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification
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DOI：
10.1093/bioinformatics/btx180
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发表时间：
2017-08-01
期刊：
影响因子：
5.8
通讯作者：
Seung, H. Sebastian
中科院分区：
文献类型：
--
作者：
Arganda-Carreras, Ignacio;Kaynig, Verena;Seung, H. Sebastian
最先进的光学和电子显微镜能够采集大型图像数据集，但定量评估数据通常涉及手动注释感兴趣的结构。这一过程很耗时，而且往往是评价管道中的一个主要瓶颈。为了克服这个问题，我们引入了可训练的Weka分割（TWS），这是一种机器学习工具，它利用有限数量的手动注释来训练分类器并自动分割剩余的数据。此外，TWS可以提供无监督的分割学习方案（聚类），并可以定制以采用用户设计的图像特征或分类器。
State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This process is time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers.