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

Abstract

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 10⁶ 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/ .

Fig. 1 |. Architecture and performance of the DeepCell Kiosk.

a, Data flow through the DeepCell Kiosk. Images submitted to a running Kiosk cluster are entered into a queue. Once there, images are processed by objects called consumers, which execute all the computational operations in a given pipeline. Consumers perform most computations themselves but access deep learning by submitting data to TensorFlow Serving and capturing the result. This separation allows conventional operations and deep learning operations to occur on different types of nodes, which is essential for efficient resource allocation. After processing, the result is returned to the queue, ready to be downloaded by the user. b, Left, benchmarking of inference speed demonstrating scaling to large imaging datasets (see the Supplementary Information for details). Throughput is ultimately limited by data transfer speed. right, an analysis of cost demonstrating affordability. network costs are incurred by inter-zone network traffic in multi-zone clusters, which are more stable and scale faster than their single-zone counterparts. This cost can be avoided when users configure a single-zone cluster. Benchmarking was performed once for 1 million (M) image runs and in triplicate otherwise. Error bars, when present, represent the s.d. of replicate measurements. c, The DeepCell Kiosk enables construction and scaling of multi-model pipelines. For example, a live-cell imaging consumer can access deep learning models for both segmentation and tracking. The live-cell imaging consumer places the frames for a movie into the queue for a segmentation consumer, where they are processed in parallel. Once segmented, the images are processed by a tracking consumer to link cells together over time and to construct lineages. The results, which consist of label images of segmented and tracked cells as well as a JSOn file describing mother–daughter relationships, are uploaded to a cloud bucket, from which they can be downloaded by the user.