NVIDIA Clara Deploy Adds New Pipelines, Operators and Improved Developer Productivity

At SIIM 2020, the annual meeting of the Society for Imaging Informatics in Medicine, NVIDIA announced updates to the Clara Deploy Application Framework to…

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Nefi Alarcon
•2 min read•intermediate•
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Overview

NVIDIA announced significant updates to the Clara Deploy Application Framework at SIIM 2020, enhancing its capabilities for addressing COVID-19 and digital pathology while improving the developer experience. The latest public release, Clara Deploy SDK 6.2, introduces new pipelines and operators that facilitate medical imaging workflows.

What You'll Learn

1

How to ingest data from PACS using the DICOM adapter in Clara Deploy

2

Why using cloud-native paradigms like Kubernetes is beneficial for medical imaging applications

3

How to implement AI operators for image segmentation in medical imaging pipelines

Key Questions Answered

What new features were added to the Clara Deploy Application Framework?
The latest release of Clara Deploy includes a reference pipeline for detecting COVID-19 in CT datasets, new operators for digital pathology, and enhancements to the developer experience, such as REST API access to the DICOM adapter.
How does the Clara Deploy framework facilitate processing of medical imaging data?
Clara Deploy allows ingestion of large datasets from sources like PACS, encapsulates medical imaging logic in operators, and executes jobs at scale using cloud-native technologies like containers and Kubernetes orchestration.

Technologies & Tools

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Data Format
Dicom
Used for ingesting medical imaging data from PACS.
Orchestration
Kubernetes
Facilitates cloud-native execution of medical imaging jobs.

Key Actionable Insights

1
Utilize the new DICOM adapter REST APIs to streamline the pre-processing of medical imaging data.
This enhancement allows developers to select the best DICOM series for processing, improving the efficiency of workflows in medical imaging applications.
2
Leverage the multi AI CT pipelines with Shared Memory FastIO for improved performance.
This feature enables faster read and write operations, which is crucial for handling large medical imaging datasets effectively.