DeepCell 自助服务亭：使用 Kubernetes 扩展支持深度学习的细胞图像分析。
DeepCell Kiosk: scaling deep learning-enabled cellular image analysis with Kubernetes.
机构信息
Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.
Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
出版信息
Nat Methods. 2021 Jan;18(1):43-45. doi: 10.1038/s41592-020-01023-0. Epub 2021 Jan 4.
Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 10 1-megapixel images in ~5.5 h for ~US$250, with a cost below US$100 achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console ; a persistent deployment is available at https://deepcell.org/ .
深度学习正在改变生物图像的分析方式，但将这些模型应用于大型数据集仍然具有挑战性。在这里，我们描述了 DeepCell Kiosk，这是一款云原生软件，可以动态扩展深度学习工作流程以适应大型成像数据集。为了展示该软件的可扩展性和可负担性，我们在大约 5.5 小时内从 10 张 100 万像素的图像中识别出细胞核，费用约为 250 美元，具体取决于集群配置，成本可低至 100 美元以下。DeepCell Kiosk 可在 https://github.com/vanvalenlab/kiosk-console 下载；永久部署可在 https://deepcell.org/ 获得。
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