插件
计算机科学
深度学习
人工智能
分割
云计算
模式识别(心理学)
图像分割
网(多面体)
机器学习
操作系统
几何学
数学
作者
Thorsten Falk,Dominic Mai,Robert Bensch,Özgün Çiçek,Ahmed Abdulkadir,Yassine Marrakchi,Anton Böhm,Jan Deubner,Zoë Jäckel,Katharina Seiwald,Alexander Dovzhenko,Olaf Tietz,Cristina Dal Bosco,Seán Walsh,Deniz Saltukoglu,Tuan Leng Tay,Marco Prinz,Klaus Palme,Matias Simons,Ilka Diester,Thomas Brox,Olaf Ronneberger
出处
期刊:Nature Methods
[Springer Nature]
日期:2018-12-04
卷期号:16 (1): 67-70
被引量:1594
标识
DOI:10.1038/s41592-018-0261-2
摘要
U-Net is a generic deep-learning solution for frequently occurring quantification tasks such as cell detection and shape measurements in biomedical image data. We present an ImageJ plugin that enables non-machine-learning experts to analyze their data with U-Net on either a local computer or a remote server/cloud service. The plugin comes with pretrained models for single-cell segmentation and allows for U-Net to be adapted to new tasks on the basis of a few annotated samples.
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