DeepCell Kiosk: scaling deep learning-enabled cellular image analysis with Kubernetes

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(6) 1-megapixel images in similar to 5.5h for similar to 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/.

Automated summary

Deep learning is changing the analysis of biological images, and the DeepCell Kiosk is a cloud-native software that can dynamically scale deep learning workflows for large imaging datasets. This software allows for efficient identification of cell nuclei in large-scale images at a relatively low cost, demonstrating scalability and affordability.