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.
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。
