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Clara AI Vnet Pipeline

This asset requires the Clara Deploy SDK. Follow the instructions on the Clara Ansible page to install the Clara Deploy SDK.

Overview

Vnet Segmentation pipelines are defined using the Pipeline definition language (that Clara supports). There are two pipelines defined with Vnet segmentation. "ct-vnetseg.yaml" pipeline performs segmentation on a CT abdominal volume, "ct-recon-vnetseg.yaml" pipeline first performs reconstruction followed by Vnet Segmentation within the same pipeline.

Operators

Vnet Segmentation pipeline (ct-vnetseg.yaml) definition consists of 4 operators (dicom-reader, ai-vnet, dicom-writer, register-dicom-results). Dicom-reader operator converts input DICOM data into MHD format. Ai-vnet segments the input data coming from dicom-reader. Dicom-writer converts the segmented volume mask into DICOM format. Register-dicom-results operator transfers the DICOM volume (from dicom-writer) to configured PACS destination.

Recon+Vnet Segmentation pipeline (ct-recon-vnetseg.yaml) definition consists of 5 operators (dicom-reader, recon-operator, ai-vnet, dicom-writer, register-dicom-results). Dicom-reader operator converts input DICOM data into MHD format. Recon-operator reconstructs the input data coming from dicom-reader. Ai-vnet segments the reconstructed data coming from recon-operator. Dicom-writer converts the segmented volume mask from ai-vnet into DICOM format. Register-dicom-results operator transfers the DICOM volume (from dicom-writer) to configured PACS destination.

All operators used in both pipelines are explained below in detail.

• dicom-reader: This operator is used in both pipelines.

  Input dicom data is mounted on /input folder and the output from this operator goes into mounted /output folder. Dicom-reader operator reads in the input DICOM image, converts them into MHD. Output of dicom-reader becomes input of recon-operator as specified in the pipeline.

  Dicom-reader is the first container in the pipeline. For the first container, "from" field is not required in the "input" definition. Dicom-Adapter will pick the first operator in the pipeline and will send the data to its mounted /input folder.

• recon-operator: Recon-operator is used only in "ct-recon-vnetseg.yaml" pipeline.

  Recon operators takes the result from dicom-reader and mounts it to /app/in folder. Recon-operator has 3 output folders. 'out' folder gets the actual reconstructed volume. 'logs' folder gets all logs from recon-operator. 'geom' folder contains the geometry file created by recon-operator. Recon-operator defines the reconstruction parameters under 'variables'. All parameters are defined in detail in recon operator definition. These variables are passed by platform as environment variables to the recon-operator at runtime. Reconstruction is GPU accelerated, and the need for GPU is specified with 'request' under recon-operator definition.

• ai-vnet: This operator is used in both the pipelines. Ai-vnet's definition in ct-recon-vnetseg.yaml is described below.

  Ai-vnet operator takes the result from recon-operator and mounts it to /app/input folder. Ai-vnet's output goes into /app/output folder. This operator defines the segmentation parameters under 'variables'. All parameters are defined in detail in Vnet operator definition. These variables are passed by platform as environment variables to the ai-vnet operator at runtime.

  This operator uses TRTIS service to make inference. Models must be copied at pre-configured location specified by Clara platform as NVIDIA_CLARA_SERVICE_DATA_PATH.

  In order for operators to contact the TRTIS service, users must define an http connection in the service definition, in the form of a name and the port which is exposed by the service container. The name for the port is mounted as an environment variable in the operators that declare the need for that service. The value of the environment variable is the URI of the service. In the example above, the environment variable NVIDIA_CLARA_TRTISURI will get mounted as an environment variable in the ai-vnet operator. The value of the variable will be :8000, where the value of the IP is assigned after the service is deployed.

Note Input field for ai-vnet operator will be different for ct-vnetseg.yaml pipeline. In ct-vnetseg.yaml pipeline, input to ai-vnet is coming from dicom-reader.

• dicom-writer: Segmented mask goes as input to dicom-writer operator and is mounted on /input folder. Dicom-writer writes the results, on /output folder.

• register-dicom-results: Takes the output from dicom-writer operator and sends the dicom images to a pre-configured PACS destination.

  'command' field specifies the startup for register-results operator with several arguments. Register-results operator uses dicom adapter configurations to register results. "--agent" field is used as a filter to query for results published by pipelines. In this pipeline, "ClaraSCU" is configured as ae-title for the scu services. "--data" argument specifies the name of destination PACS configurations. In example below "MYPACS" is one such configuration, and it can have multiple values if the results has to be shipped to multiple PACS destinations". Refer to the dicom-server-config.yaml shipped with the SDK. For additional details refer to results-services operator and results

License

An End User License Agreement is included with the product. By pulling and using the Clara Deploy asset on NGC, you accept the terms and conditions of these licenses.

Suggested Reading

Release Notes, the Getting Started Guide, and the SDK itself are available at the NVIDIA Developer forum: (https://developer.nvidia.com/clara).

For answers to any questions you may have about this release, visit the NVIDIA Devtalk forum: (https://devtalk.nvidia.com/default/board/362/clara-sdk/).