DeepCell 2.0: Automated cloud deployment of deep learning models for large-scale cellular image analysis

Deep learning is transforming the ability of life scientists to extract information from images. While these techniques have superior accuracy in comparison to conventional approaches and enable previously impossible analyses, their unique hardware and software requirements have prevented widespread adoption by life scientists. To meet this need, we have developed DeepCell 2.0, an open source library for training and delivering deep learning models with cloud computing. This library enables users to configure and manage a cloud deployment of DeepCell 2.0 on all commonly used operating systems. Using single-cell segmentation as a use case, we show that users with suitable training data can train models and analyze data with those models through a web interface. We demonstrate that by matching analysis tasks with their hardware requirements, we can efficiently use computational resources in the cloud and scale those resources to meet demand, significantly reducing the time necessary for large-scale image analysis. By reducing the barriers to entry, this work will empower life scientists to apply deep learning methods to their data. A persistent deployment is available at http://www.deepcell.org

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 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/

Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org

DeepCell 2.0: Automated cloud deployment of deep learning models for large-scale cellular image analysis

Deep learning is transforming the ability of life scientists to extract information from images. While these techniques have superior accuracy in comparison to conventional approaches and enable previously impossible analyses, their unique hardware and software requirements have prevented widespread adoption by life scientists. To meet this need, we have developed DeepCell 2.0, an open source library for training and delivering deep learning models with cloud computing. This library enables users to configure and manage a cloud deployment of DeepCell 2.0 on all commonly used operating systems. Using single-cell segmentation as a use case, we show that users with suitable training data can train models and analyze data with those models through a web interface. We demonstrate that by matching analysis tasks with their hardware requirements, we can efficiently use computational resources in the cloud and scale those resources to meet demand, significantly reducing the time necessary for large-scale image analysis. By reducing the barriers to entry, this work will empower life scientists to apply deep learning methods to their data. A persistent deployment is available at http://www.deepcell.org

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 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/

Accurate cell tracking and lineage construction in live-cell imaging experiments with deep learning

Live-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org

Tinzaparin vs warfarin for treatment of acute venous thromboembolism in patients with active cancer: A randomized clinical trial

IMPORTANCE Low-molecular-weight heparin is recommended over warfarin for the treatment of acute venous thromboembolism (VTE) in patients with active cancer largely based on results of a single, large trial. OBJECTIVE To study the efficacy and safety of tinzaparin vs warfarin for treatment of acute, symptomatic VTE in patients with active cancer. DESIGN, SETTINGS, AND PARTICIPANTS A randomized, open-label study with blinded central adjudication of study outcomes enrolled patients in 164 centers in Asia, Africa, Europe, and North, Central, and South America between August 2010 and November 2013. Adult patients with active cancer (defined as histologic diagnosis of cancer and receiving anticancer therapy or diagnosed with, or received such therapy, within the previous 6 months) and objectively documented proximal deep vein thrombosis (DVT) or pulmonary embolism, with a life expectancy greater than 6 months and without contraindications for anticoagulation, were followed up for 180 days and for 30 days after the last study medication dose for collection of safety data. INTERVENTIONS Tinzaparin (175 IU/kg) once daily for 6 months vs conventional therapy with tinzaparin (175 IU/kg) once daily for 5 to 10 days followed by warfarin at a dose adjusted to maintain the international normalized ratio within the therapeutic range (2.0-3.0) for 6 months. MAIN OUTCOMES AND MEASURES Primary efficacy outcome was a composite of centrally adjudicated recurrent DVT, fatal or nonfatal pulmonary embolism, and incidental VTE. Safety outcomes included major bleeding, clinically relevant nonmajor bleeding, and overall mortality. RESULTS Nine hundred patients were randomized and included in intention-to-treat efficacy and safety analyses. Recurrent VTE occurred in 31 of 449 patients treated with tinzaparin and 45 of 451 patients treated with warfarin (6-month cumulative incidence, 7.2% for tinzaparin vs 10.5% for warfarin; hazard ratio [HR], 0.65 [95% CI, 0.41-1.03]; P = .07). There were no differences in major bleeding (12 patients for tinzaparin vs 11 patients for warfarin; HR, 0.89 [95% CI, 0.40-1.99]; P = .77) or overall mortality (150 patients for tinzaparin vs 138 patients for warfarin; HR, 1.08 [95% CI, 0.85-1.36]; P = .54). A significant reduction in clinically relevant nonmajor bleeding was observed with tinzaparin (49 of 449 patients for tinzaparin vs 69 of 451 patients for warfarin; HR, 0.58 [95% CI, 0.40-0.84]; P = .004). CONCLUSIONS AND RELEVANCE Among patients with active cancer and acute symptomatic VTE, the use of full-dose tinzaparin (175 IU/kg) daily compared with warfarin for 6 months did not significantly reduce the composite measure of recurrent VTE and was not associated with reductions in overall mortality or major bleeding, but was associated with a lower rate of clinically relevant nonmajor bleeding. Further studies are needed to assess whether the efficacy outcomes would be different in patients at higher risk of recurrent VTE