间隙
计算机科学
显微镜
图像(数学)
工作流程
分辨率(逻辑)
人工智能
计算机视觉
光学
物理
数据库
医学
泌尿科
作者
Qingjie Zhu,Yi Shao,Zhicheng Wang,Xingjun Chen,Chunqiong Li,Zihan Liang,Mingyue Jia,Qingchun Guo,Hu Zhao,Lei Kong,Li Zhang
出处
期刊:Bioinformatics
[Oxford University Press]
日期:2021-02-26
卷期号:37 (18): 3086-3087
被引量:2
标识
DOI:10.1093/bioinformatics/btab144
摘要
Microscopy technology plays important roles in many biological research fields. Solvent-cleared brain high-resolution (HR) 3D image reconstruction is an important microscopy application. However, 3D microscopy image generation is time-consuming and expensive. Therefore, we have developed a deep learning framework (DeepS) for both image optical sectioning and super resolution microscopy.Using DeepS to perform super resolution solvent-cleared mouse brain microscopy 3D image yields improved performance in comparison with the standard image processing workflow. We have also developed a web server to allow online usage of DeepS. Users can train their own models with only one pair of training images using the transfer learning function of the web server.http://deeps.cibr.ac.cn.Supplementary data are available at Bioinformatics online.
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