DeepImageJ: A user-friendly environment to run deep learning models in ImageJ
DeepImageJ: A user-friendly environment to run deep learning models in ImageJ
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DOI：
10.1038/s41592-021-01262-9
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发表时间：
2021-09-30
期刊：
影响因子：
48
通讯作者：
Sage, Daniel
中科院分区：
文献类型：
--
作者：
Gomez-de-Mariscal, Estibaliz;Garcia-Lopez-de-Haro, Carlos;Sage, Daniel
DeepImageJ在ImageJ中提供了一个用户友好的解决方案，可以运行用于生物医学图像分析的训练过的深度学习模型。DeepImageJ是一个用户友好的解决方案，可以在ImageJ中通用使用预训练的深度学习模型进行生物医学图像分析。deepImageJ环境允许访问预训练深度学习模型的最大生物图像存储库（BioImage Model Zoo）。因此，非专家可以使用基于深度学习的工具轻松执行生命科学研究中的常见图像处理任务，包括像素和对象分类、实例分割、去噪或虚拟染色。DeepImageJ与现有的最先进的解决方案兼容，它配备了实用工具，供开发人员使用，以包括新的模型。最近，一些培训框架采用了deepImageJ格式，将其工作部署在该领域最常用的软件之一（ImageJ）中。除了直接使用，我们希望deepImageJ能够为生命科学应用和生物图像信息学中深度学习模型的更广泛传播和重用做出贡献。
DeepImageJ offers a user-friendly solution in ImageJ to run trained deep learning models for biomedical image analysis. It includes guiding tools for reliable analyses, contributing to the democratization of deep learning in microscopy.DeepImageJ is a user-friendly solution that enables the generic use of pre-trained deep learning models for biomedical image analysis in ImageJ. The deepImageJ environment gives access to the largest bioimage repository of pre-trained deep learning models (BioImage Model Zoo). Hence, nonexperts can easily perform common image processing tasks in life-science research with deep learning-based tools including pixel and object classification, instance segmentation, denoising or virtual staining. DeepImageJ is compatible with existing state of the art solutions and it is equipped with utility tools for developers to include new models. Very recently, several training frameworks have adopted the deepImageJ format to deploy their work in one of the most used softwares in the field (ImageJ). Beyond its direct use, we expect deepImageJ to contribute to the broader dissemination and reuse of deep learning models in life sciences applications and bioimage informatics.