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Clara Deploy SDK is being consolidated into Clara Holoscan SDK

More info https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/collections/claradeploy

Clara Deploy AI COVID-19 Classification Pipeline

CAUTION: This is NOT for diagnostics use.

This asset requires the Clara Deploy SDK. Follow the instructions on the\nClara Ansible page\nto install the Clara Deploy SDK.

DISCLAIMER

This inference pipeline was developed by NVIDIA. It is based on a segmentation and classification model developed by NVIDIA researchers in conjunction with the NIH. The Software is for Research Use Only. Software’s recommendation should not be solely or primarily relied upon to diagnose or treat COVID-19 by a Healthcare Professional. This research use only software has not been cleared or approved by FDA or any regulatory agency

This reference pipeline is designed to infer the probability of COVID-19 infection with the chest CT scan of a patient. A 3D lung segmentation model as well as a 3D classification model are used in this pipeline. A single aixial DICOM series of chest CT scan is the pipeline input, and the final output is the report of the probabilities of COVID-19 and non-COVID-19.

Once the DICOM instances are received, the pipeline is triggered to first convert the DICOM instances to a volume image in MetaImage format. This image is then used as the input to two segmentation operators concurrently, the lugn segmentation and the COVID-19 lesion segmentation. Each segmentation operator performs inference using its own segmentation AI model, and generates a labeled segmentation as a binary mask on each slice of the volume, with the lung or lung lesion labeled as 1, and the background labeled as 0.

In the next step, the original volume image along with the labeled segmentation image are used by the COVID-19 classification operator to infer the probabilities of COVID-19 using the COVID-19 classification model.

The volume images generated by the COVID-19 lesion segmentation operator and the lung segmentation operator are also used to as inputs to a volume ratio metrics operator, which calculates and saves the lesion to lung ratio.

The pipeline generates the following outputs:

Lung segmentation image in MetaImage format
COVID-19 lesion segmentation image in MetaImage format
A new DICOM series for the lung segmentation image, optionally sent to a DICOM device
A new DICOM series for the COVID-19 lesion segmentation image, optionally sent to a DICOM device
A new DICOM series of Encapsulated PDF for the AI classification results, optionally sent to a DICOM device
A new DICOM series of Encapsulated PDF for the COVID-19 lesion to lung ratio results, optionally sent to a DICOM device
The original and lung segmentation volumes in MetaImage format to the Clara Deploy Render Server.
The original and COVID-19 lesion segmentation volumes in MetaImage format to the Clara Deploy Render Server.

Pipeline Definition

This pipeline is defined in the Clara Deploy pipeline definition language. It utilizes built-in reference containers to construct the following set of operators:

The dicom-reader operator converts input DICOM data into volume images in MetaImage format format.
The lung segmentation operator performs AI inference against the NVIDIA Triton Inference Server to generate segmentation volume images.
The COVID-19 lesion segmentation operator performs AI inference against the NVIDIA Triton Inference Server to generate segmentation volume images.
The classification operator performs AI inference against the NVIDIA Triton Inference Server to infer the probability of COVID-19.
The COVID-19 lesion to lung volume ratio operator.
The dicom-seg-writer converts the lung segmentation volume image into a DICOM SEG instance with a new series instance UID but the same study instance UID of the original DICOM.
The register-dicom-output operator registers the lung DICOM SEG instance with the Clara Deploy DICOM Adapter which in turn stores the instance on external DICOM devices per configuration.
The dicom-seg-writer for COVID-19 lesion converts the COVID-19 lesion segmentation volume image into a DICOM SEG instance with a new series instance UID but the same study instance UID of the original DICOM.
The register-dicom-output operator for COVID-19 lesion registers the lesion DICOM SEG instance with the Clara Deploy DICOM Adapter which in turn stores the instance on external DICOM devices per configuration.
The dicom-report-writer converts the AI classification results into DICOM Encapsulated PDF instances with a new series instance UID but the same study instance UID of the original DICOM.
The dicom-report-writer for volume ratio metrics converts the COVID-19 lesion to lung volume ratio metrics into a DICOM Encapsulated PDF instances with a new series instance UID but the same study instance UID of the original DICOM.
The register-dicom-report operator registers the DICOM Encapsulated PDF with the Clara Deploy DICOM Adapter which in turn stores the instance on external DICOM devices per configuration.
The register-volume-images-for-rendering operator registers original and segmented volume images with the Clara Deploy Render Server for visualization.

The following is the details of pipeline definition, with comments describing each operator's functions\nas well as input and output.

api-version: 0.4.0\nname: COVID-19-pipeline\noperators:\n# dicom reader operator\n# Input: '/input' mapped directly to the input of the pipeline, which is populated by the DICOM Adaptor.\n# Output:'/output' for saving converted volume image in MHD format to file whose name\n#            is the same as the DICOM series instance ID.\n- name: dicom-reader\n  description: Converts DICOM instances into MHD, one file per DICOM series.\n  container:\n    image: clara/dicom-reader\n    tag: latest\n  input:\n  - path: /input\n  output:\n  - path: /output\n# lung-segmentation operator\n# Input: `/input` containing volume image data, MHD format, with a single volume.\n# Output: `/output` containing segmented volume image, MHD format.\n#         `/publish` containing original and segmented volume images, MHD format,\n#             along with rendering configuration file.\n- name: lung-segmentation\n  description: Segmentation of lung using DL trained model.\n  container:\n    image: clara/ai-lung\n    tag: latest\n  requests:\n    memory: 8192\n  input:\n  - from: dicom-reader\n    path: /input\n  output:\n  - path: /output\n    name: segmentation\n  - path: /publish\n    name: rendering\n  services:\n  - name: triton\n  # Triton Inference Server, required by this AI application.\n    container:\n      image: nvcr.io/nvidia/tritonserver\n      tag: 20.07-v1-py3\n      command: [\"tritonserver\", \"--model-repository=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models\"]\n    # services::connections defines how the Triton service is expected to\n    # be accessed. Clara Platform supports network (\"http\") and\n    # volume (\"file\") connections.\n    connections:\n      http:\n      # The name of the connection is used to populate an environment\n      # variable inside the operator's container during execution.\n      # This AI application inside the container needs to read this variable to\n      # know the IP and port of Triton in order to connect to the service.\n      - name: NVIDIA_CLARA_TRTISURI\n        port: 8000\n      # Some services need a specialized or minimal set of hardware. In this case\n      # NVIDIA Triton Inference Server [Triton] requires at least one GPU to function.\n# dicom segmentation writer operator\n# Input1: `/input` containing a volume image file, in MHD format, name matching the DICOM series instance UID.\n# Input2: `/dcm` containing original DICOM instances, i.e. dcm file.\n# Output: `/output` containing the DICOM instances converted from the volume image, with updated attributes\n#         based on original DICOM instances.\n- name: dicom-seg-writer\n  description: Converts MHD into DICOM instance with attributes based on the original instances.\n  container:\n    image: clara/dicom-seg-writer\n    tag: latest\n  variables:\n    NVIDIA_SEG_LABELS: \"[\\\"Lung-seg\\\"]\"\n  input:\n  - from: lung-segmentation\n    name: segmentation\n    path: /input\n  - path: /dcm\n  output:\n  - path: /output\n    name: dicom\n  - path: /logs\n    name: logs\n# register-volume-images-for-rendering operator\n# Input: Published original and segmented volume images, MHD format, along with rendering configuration file\n#        from the segmentation operator.\n# Output: N/A. Input data will be sent to the destination, namely `renderserver` for Render Server DataSet Service.\n- name: register-volume-images-for-rendering\n  description: Register volume images, MHD format, for rendering.\n  container:\n    image: clara/register-results\n    tag: latest\n    command: [\"python\", \"register.py\", \"--agent\", \"renderserver\"]\n  input:\n  - from: lung-segmentation\n    name: rendering\n    path: /input\n# register-dicom-output operator\n# Input: `/input` containing DICOM instances in the named output, `dicom` from dicom-seg-writer operator.\n# Output: N/A. Input data will be sent to the destinations, namely DICOM devices, by the Clara DICOM SCU agent.\n- name: register-dicom-output\n  description: Register converted DICOM instances with Results Service to be sent to external DICOM devices.\n  container:\n    image: clara/register-results\n    tag: latest\n    command: [\"python\", \"register.py\", \"--agent\", \"ClaraSCU\", \"--data\", \"[\\\"MYPACS\\\"]\"]\n  input:\n  - from: dicom-seg-writer\n    name: dicom\n    path: /input\n## Begin: COVID-19 Lesion\n# covid-lesion-segmentation operator\n# Input: `/input` containing volume image data, MHD format, with a single volume.\n# Output: `/output` containing segmented volume image, MHD format.\n#         `/publish` containing original and segmented volume images, MHD format,\n#             along with rendering configuration file.\n- name: covid-lesion-segmentation\n  description: Segmentation of COVID-19 GGO using DL trained model.\n  container:\n    image: clara/ai-covid-lesion\n    tag: latest\n  requests:\n    memory: 8192\n  input:\n  - from: dicom-reader\n    path: /input\n  output:\n  - path: /output\n    name: segmentation\n  - path: /publish\n    name: rendering\n  services:\n  - name: triton\n  # Triton Inference Server, required by this AI application.\n    container:\n      image: nvcr.io/nvidia/tritonserver\n      tag: 20.07-v1-py3\n      command: [\"tritonserver\", \"--model-repository=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models\"]\n    # services::connections defines how the Triton service is expected to\n    # be accessed. Clara Platform supports network (\"http\") and\n    # volume (\"file\") connections.\n    connections:\n      http:\n      # The name of the connection is used to populate an environment\n      # variable inside the operator's container during execution.\n      # This AI application inside the container needs to read this variable to\n      # know the IP and port of Triton in order to connect to the service.\n      - name: NVIDIA_CLARA_TRTISURI\n        port: 8000\n      # Some services need a specialized or minimal set of hardware. In this case\n      # NVIDIA Triton Inference Server [Triton] requires at least one GPU to function.\n# dicom covid-lesion segmentation writer operator\n# Input1: `/input` containing a volume image file, in MHD format, name matching the DICOM series instance UID.\n# Input2: `/dcm` containing original DICOM instances, i.e. dcm file.\n# Output: `/output` containing the DICOM instances converted from the volume image, with updated attributes\n#         based on original DICOM instances.\n- name: dicom-seg-writer-covid-lesion\n  description: Converts MHD into DICOM instance with attributes based on the original instances.\n  container:\n    image: clara/dicom-seg-writer\n    tag: latest\n  variables:\n    NVIDIA_SEG_LABELS: \"[\\\"GroundGlass-seg\\\"]\"\n  input:\n  - from: covid-lesion-segmentation\n    name: segmentation\n    path: /input\n  - path: /dcm\n  output:\n  - path: /output\n    name: dicom\n  - path: /logs\n    name: logs\n# register-volume-images-for-rendering operator\n# Input: Published original and segmented volume images, MHD format, along with rendering configuration file\n#        from the segmentation operator.\n# Output: N/A. Input data will be sent to the destination, namely `renderserver` for Render Server DataSet Service.\n- name: register-covid-lesion-volume-images-for-rendering\n  description: Register volume images, MHD format, for rendering.\n  container:\n    image: clara/register-results\n    tag: latest\n    command: [\"python\", \"register.py\", \"--agent\", \"renderserver\"]\n  input:\n  - from: covid-lesion-segmentation\n    name: rendering\n    path: /input\n# register-dicom-output operator\n# Input: `/input` containing DICOM instances in the named output, `dicom` from dicom-seg-writer operator.\n# Output: N/A. Input data will be sent to the destinations, namely DICOM devices, by the Clara DICOM SCU agent.\n- name: register-covid-lesion-dicom-output\n  description: Register converted DICOM instances with Results Service to be sent to external DICOM devices.\n  container:\n    image: clara/register-results\n    tag: latest\n    command: [\"python\", \"register.py\", \"--agent\", \"ClaraSCU\", \"--data\", \"[\\\"MYPACS\\\"]\"]\n  input:\n  - from: dicom-seg-writer-covid-lesion\n    name: dicom\n    path: /input\n## END: COVID-19 lesion segmentation\n# COVID-19 Classification operator\n# Input: original image (DICOM series converted image) and segmented volume images, MHD format.\n# Output: CSV file for classification resuls: the probabilities for both COVID-19 and non-COVID-19.\n- name: classification-covid-19\n  description: Classification of COVID-19 using DL model with original and segmentation images.\n  container:\n    image: clara/ai-covid-19\n    tag: latest\n  requests:\n    memory: 4096\n  input:\n  - from: lung-segmentation\n    name: segmentation\n    path: /label_image\n  - from: dicom-reader\n    path: /input\n  output:\n  - path: /output\n    name: classification\n  services:\n  - name: triton\n  # Triton Inference Server, required by this AI application.\n    container:\n      image: nvcr.io/nvidia/tritonserver\n      tag: 20.07-v1-py3\n      command: [\"tritonserver\", \"--model-repository=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models\"]\n    # services::connections defines how the Triton service is expected to\n    # be accessed. Clara Platform supports network (\"http\") and\n    # volume (\"file\") connections.\n    connections:\n      http:\n      # The name of the connection is used to populate an environment\n      # variable inside the operator's container during execution.\n      # This AI application inside the container needs to read this variable to\n      # know the IP and port of Triton in order to connect to the service.\n      - name: NVIDIA_CLARA_TRTISURI\n        port: 8000\n      # Some services need a specialized or minimal set of hardware. In this case\n      # NVIDIA Triton Inference Server [Triton] requires at least one GPU to function.\n# dicom report writer operator\n# Input1: `/input` containing a classification result file, in txt or csv format.\n# Input2: `/dcm` containing original DICOM instances, i.e. dcm file.\n# Output: `/output` containing the DICOM report instances converted with attributes\n#         from the original DICOM instances.\n- name: dicom-report-writer\n  description: Create DICOM AI Results report, with attributes based on the original instances.\n  container:\n    image: clara/dicom-report-writer\n    tag: latest\n  variables:\n    NVIDIA_DICOM_REPORT_TYPE: 'pdf'\n    NVIDIA_AI_MODEL_CREATOR: 'NVIDIA/NIH'\n    NVIDIA_AI_MODEL_NAME: 'Clara CT Classification COVID-19'\n    NVIDIA_AI_MODEL_VERSION: '1.0'\n  input:\n  - from: classification-covid-19\n    name: classification\n    path: /input\n  - path: /dcm\n  output:\n  - path: /output\n    name: dicom\n  - path: /logs\n    name: logs\n# register-dicom-report operator\n# Input: `/input` containing DICOM instances in the named output, `dicom` from dicom-seg-writer operator.\n# Output: N/A. Input data will be sent to the destinations, namely DICOM devices, by the Clara DICOM SCU agent.\n- name: register-dicom-report\n  description: Register DICOM instances with Results Service to be sent to external DICOM devices.\n  container:\n    image: clara/register-results\n    tag: latest\n    command: [\"python\", \"register.py\", \"--agent\", \"ClaraSCU\", \"--data\", \"[\\\"MYPACS\\\"]\"]\n  input:\n  - from: dicom-report-writer\n    name: dicom\n    path: /input\n## Begin: covid_lung_ratio\n# COVID-19 Classification operator\n# Input: COVID-19 lesion volume image and segmented lung volume images, MHD format.\n# Output: txt file for volume ratio metrics.\n- name: covid-lung-volume-ratio\n  description: Classification of COVID-19 using DL model with original and segmentation images.\n  container:\n    image: clara/ai-volume-ratio\n    tag: latest\n  requests:\n    memory: 2048\n  input:\n  - from: lung-segmentation\n    name: segmentation\n    path: /label_image\n  - from: covid-lesion-segmentation\n    name: segmentation\n    path: /input\n  output:\n  - path: /output\n    name: metrics\n# dicom report writer operator\n# Input1: `/input` containing a classification result file, in txt or csv format.\n# Input2: `/dcm` containing original DICOM instances, i.e. dcm file.\n# Output: `/output` containing the DICOM report instances converted with attributes\n#         from the original DICOM instances.\n- name: dicom-report-writer-metrics\n  description: Create DICOM AI Results report, with attributes based on the original instances.\n  container:\n    image: clara/dicom-report-writer\n    tag: latest\n  variables:\n    NVIDIA_DICOM_REPORT_TYPE: 'pdf'\n    NVIDIA_AI_MODEL_CREATOR: 'NVIDIA/NIH'\n    NVIDIA_AI_MODEL_NAME: 'Clara COVID-19 Lung Lesion'\n    NVIDIA_AI_MODEL_VERSION: '1.0'\n  input:\n  - from: covid-lung-volume-ratio\n    name: metrics\n    path: /input\n  - path: /dcm\n  output:\n  - path: /output\n    name: dicom\n  - path: /logs\n    name: logs\n# register-dicom-report operator\n# Input: `/input` containing DICOM instances in the named output, `dicom` from dicom-seg-writer operator.\n# Output: N/A. Input data will be sent to the destinations, namely DICOM devices, by the Clara DICOM SCU agent.\n- name: register-dicom-report-metrics\n  description: Register DICOM instances with Results Service to be sent to external DICOM devices.\n  container:\n    image: clara/register-results\n    tag: latest\n    command: [\"python\", \"register.py\", \"--agent\", \"ClaraSCU\", \"--data\", \"[\\\"MYPACS\\\"]\"]\n  input:\n  - from: dicom-report-writer-metrics\n    name: dicom\n    path: /input\n## End covid_lung_ratio\n

Executing the Pipeline

Please refer to the Quick Start Guide section on how to register a pipeline, configure the DICOM Adapter, and execute the pipeline.

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. For Clara Deploy AI COVID-19 Classification Pipeline you\naccept the terms and conditions that are mentioned in the license file inside the package.

Suggested Reading

Release Notes, the Getting Started Guide, and the SDK itself are available at the NVIDIA Developer forum.

For answers to any questions you may have about this release, visit the NVIDIA Devtalk forum.

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Follow the instructions on the\n[Clara Bootstrap](https://ngc.nvidia.com/catalog/model-scripts/nvidia:clara:clara_bootstrap) page\nto install the Clara Deploy SDK.\n\n## DISCLAIMER\n\nThis inference pipeline was developed by NVIDIA. It is based on a segmentation and classification model developed by NVIDIA researchers in conjunction with the NIH. This training and inference pipeline is licensed for research and development only. It is not approved by any regulatory agency, and is not intended for clinical use\n\n---\n\nThis reference pipeline is designed to infer the probability of COVID-19 infection with the lung CT scan of a patient. A 3D lung segmentation model as well as a 3D classification model are used in this pipeline. A single aixial DICOM series of lung CT scan is the pipeline input, and the final output is the probabilities of COVID-19 and non-COVID-19 in csv format.\n\nOnce the DICOM instances are received, the pipeline is triggered to first convert the DICOM instances to a volume image in [MetaImage format](https://itk.org/Wiki/ITK/MetaIO/Documentation). This image is then used as the input to the lung segmentation operator which performas inference using the segmentation AI model, and generates a labeled segmentation as a binary mask on each slice of the volume, with the lung labeled as 1, and the background labeled as 0. In the next step, the original volume image along with the labeled segmentation image are used by the COVID-19 classification operator to infer the probabilities of COVID-19 using the COVID-19 classification model.\n\nThe pipeline generates the following outputs:\n- Lung segmentation image in [MetaImage format](https://itk.org/Wiki/ITK/MetaIO/Documentation)\n- A new DICOM series for the segmentation image, optionally sent to a DICOM device\n- Probabilities of COVID-19 and non-COVID-19 in csv format \n- The original and segmented volumes in [MetaImage format](https://itk.org/Wiki/ITK/MetaIO/Documentation) to the Clara Deploy\nRender Server for visualization.\n\n## Pipeline Definition\n\nThis pipeline is defined in the Clara Deploy pipeline definition language. It utilizes built-in reference containers to construct the following set of operators:\n\n- The dicom-reader operator converts input DICOM data into volume images in [MetaImage format](https://itk.org/Wiki/ITK/MetaIO/Documentation) format.\n- The segmentation operator performs AI inference against the NVIDIA [Triton Inference Server](https://docs.nvidia.com/deeplearning/sdk/triton-inference-server-guide/docs/index.html), formerly known as TRTIS, to generate segmentation volume images.\n- The classification operator performs AI inference against the NVIDIA [Triton Inference Server](https://docs.nvidia.com/deeplearning/sdk/triton-inference-server-guide/docs/index.html) to infer the probability of COVID-19. \n- The dicom-writer converts the segmented volume image into DICOM instances with a new series instance UID but the same study instance UID of the original DICOM.\n- The register-dicom-output operator registers the DICOM instances with the Clara Deploy DICOM Adapter which in turn stores the instance on external DICOM devices per configuration.\n- The register-volume-images-for-rendering operator registers original and segmented volume images with the Clara Deploy Render Server for visualization.\n\nThe following is the details of pipeline definition, with comments describing each operator's functions\nas well as input and output.\n\n```yaml\napi-version: 0.4.0\nname: COVID-19-pipeline\noperators:\n# dicom reader operator\n# Input: '/input' mapped directly to the input of the pipeline, which is populated by the DICOM Adaptor.\n# Output:'/output' for saving converted volume image in MHD format to file whose name\n# is the same as the DICOM series instance ID.\n- name: dicom-reader\n description: Converts DICOM instances into MHD, one file per DICOM series.\n container:\n image: clara/dicom-reader\n tag: 0.6.0-2005.1\n input:\n - path: /input\n output:\n - path: /output\n# lung-segmentation operator\n# Input: `/input` containing volume image data, MHD format, with a single volume.\n# Output: `/output` containing segmented volume image, MHD format.\n# `/publish` containing original and segmented volume images, MHD format,\n# along with rendering configuration file.\n- name: lung-segmentation\n description: Segmentation of lung using DL trained model.\n container:\n image: clara/ai-lung\n tag: latest\n input:\n - from: dicom-reader\n path: /input\n output:\n - path: /output\n name: segmentation\n - path: /publish\n name: rendering\n services:\n - name: trtis\n # Triton Inference Server, aka TRTIS, required by this AI application.\n container:\n image: nvcr.io/nvidia/tensorrtserver\n tag: 19.08-py3\n command: [\"trtserver\", \"--model-store=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models\"]\n requests:\n gpu: 1\n # services::connections defines how the TRTIS service is expected to\n # be accessed. Clara Platform supports network (\"http\") and\n # volume (\"file\") connections.\n connections:\n http:\n # The name of the connection is used to populate an environment\n # variable inside the operator's container during execution.\n # This AI application inside the container needs to read this variable to\n # know the IP and port of TRTIS in order to connect to the service.\n - name: NVIDIA_CLARA_TRTISURI\n port: 8000\n # Some services need a specialized or minimal set of hardware. In this case\n # NVIDIA Tensor RT Inference Server [TRTIS] requires at least one GPU to function.\n# dicom writer operator\n# Input1: `/input` containing a volume image file, in MHD format, name matching the DICOM series instance UID.\n# Input2: `/dicom` containing original DICOM instances, i.e. dcm file.\n# Output: `/output` containing the DICOM instances converted from the volume image, with updated attributes\n# based on original DICOM instances.\n- name: dicom-writer\n description: Converts MHD into DICOM instance with attributes based on the original instances.\n container:\n image: clara/dicom-writer\n tag: latest\n input:\n - from: lung-segmentation\n name: segmentation\n path: /input\n - path: /dicom\n output:\n - path: /output\n name: dicom\n# register-volume-images-for-rendering operator\n# Input: Published original and segmented volume images, MHD format, along with rendering configuration file\n# from the segmentation operator.\n# Output: N/A. Input data will be sent to the destination, namely `renderserver` for Render Server DataSet Service.\n- name: register-volume-images-for-rendering\n description: Register volume images, MHD format, for rendering.\n container:\n image: clara/register-results\n tag: latest\n command: [\"python\", \"register.py\", \"--agent\", \"renderserver\"]\n input:\n - from: lung-segmentation\n name: rendering\n path: /input\n# register-dicom-output operator\n# Input: `/input` containing DICOM instances in the named output, `dicom` from dicom-writer operator.\n# Output: N/A. Input data will be sent to the destinations, namely DICOM devices, by the Clara DICOM SCU agent.\n- name: register-dicom-output\n description: Register converted DICOM instances with Results Service to be sent to external DICOM devices.\n container:\n image: clara/register-results\n tag: latest\n command: [\"python\", \"register.py\", \"--agent\", \"ClaraSCU\", \"--data\", \"[\\\"MYPACS\\\"]\"]\n input:\n - from: dicom-writer\n name: dicom\n path: /input\n# COVID-19 Classification operator\n# Input: original image (DICOM series converted image) and segmented volume images, MHD format.\n# Output: CSV file for classification result: other, covid-early, covid-advanced.\n- name: classification-covid-19\n description: Classification of COVID-19 using DL model with original and segmentation images.\n container:\n image: clara/ai-covid-19\n tag: latest\n input:\n - from: lung-segmentation\n name: segmentation\n path: /label_image\n - from: dicom-reader\n path: /input\n output:\n - path: /output\n name: classification\n services:\n - name: trtis\n # TensorRT Inference Server, required by this AI application.\n container:\n image: nvcr.io/nvidia/tensorrtserver\n tag: 19.08-py3\n command: [\"trtserver\", \"--model-store=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models\"]\n requests:\n gpu: 1\n # services::connections defines how the TRTIS service is expected to\n # be accessed. Clara Platform supports network (\"http\") and\n # volume (\"file\") connections.\n connections:\n http:\n # The name of the connection is used to populate an environment\n # variable inside the operator's container during execution.\n # This AI application inside the container needs to read this variable to\n # know the IP and port of TRTIS in order to connect to the service.\n - name: NVIDIA_CLARA_TRTISURI\n port: 8000\n # Some services need a specialized or minimal set of hardware. In this case\n # NVIDIA Tensor RT Inference Server [TRTIS] requires at least one GPU to function.\n```\n\n## Executing the Pipeline\n\nPlease refer to the `Quick Start Guide` section on how to register a pipeline, configure the DICOM Adapter, and execute the pipeline.\n\n## License\n\nAn [End User License Agreement](https://developer.nvidia.com/nvidia-clara-sdk-license) is included with the product. By pulling and using the Clara Deploy asset on NGC, you accept the terms and conditions of these licenses. For Clara Deploy AI COVID-19 Classification Pipeline you \naccept the terms and conditions that are mentioned in the license file inside the package.\n\n## Suggested Reading\n\nRelease Notes, the Getting Started Guide, and the SDK itself are available at the [NVIDIA Developer forum](https://developer.nvidia.com/clara).\n\nFor answers to any questions you may have about this release, visit the [NVIDIA Devtalk forum](https://forums.developer.nvidia.com/healthcare/clara-deploy-sdk).","UPLOAD_COMPLETE","releaseType","createdDate",["D",1589407780346],"updatedDate","totalSizeInBytes",391295595,"totalFileCount",5,"isSigned","malwareScanStatus","isScanned","hasReleaseNotes","setup","1. CD into a directory where pipelines will be stored, e.g.\n```bash\nmkdir -p ~/.clara/pipelines\ncd ~/.clara/pipelines\n```\n2. Use clara pull pipeline to download a pipeline definition:\n```bash\nclara pull pipeline clara_ai_covid19_pipeline\n```\n3. Unzip test data:\n```bash\nunzip app_lung-input_v1.zip -d input\n```\n4. Unzip and Install model:\n```bash\nsudo unzip app_covid-19-model_v1.zip -d /clara/common/models/\n```\n5. Install pipeline:\n```bash\nclara create pipelines -p COVID-19-pipeline.yaml\n```\n7. Verify that the pipeline is correctly stored\n```bash\nclara list pipelines\n```\n8. Verify that the stored pipeline is correct by examining the output of the following command:\n```bash\nkubectl describe pipeline \n```","hasSetup","quickStartGuide","# How to Run a Reference Pipeline\n\nClara Deploy SDK comes bundled with a set of reference pipelines. This section provides general guidelines on how to execute them on your local development machine. For information specific to a reference pipeline, please view the documentation of that pipeline.\n\n## Download the Reference Pipeline\n\nPlease refer to `Setup` section for downloading this Pipeline.\n\n## Run Reference Pipelines by Sending DICOM Instances Over Network\n\nMany reference pipelines are triggered by Clara DICOM Adaptor after it has received DICOM instances from external sender to one of its mapped AE Titles. This section explains the process with an example pipeline COVID-19 Pipeline. \n\n### External DICOM Sender and DICOM Receiver\n\nYou need an external DICOM Service Class User (SCU) application to send images to the Clara DICOM Adapter (acting as a DICOM SCP). Similarly, when your pipeline finishes executing, you may want to send the output to an external DICOM receiver.\n\nFor this example, we will use the open-source DICOM toolkit [DCMTK](https://dicom.offis.de/dcmtk.php.en) to provide the *external* DICOM sender and DICOM receiver functionality.\n\n#### Install DCMTK\n\nInstall DCMTK utilities by issuing the following command:\n\n```bash\nsudo apt-get install dcmtk\n```\n\n#### Setup an External DICOM Receiver\n\nYou can use the storescp utility as an external DICOM receiver. Open a terminal and run the following command to set up a DICOM Receiver. Create a directory in your machine where DICOM images produced by Clara Deploy SDK will be stored. We will refer to that directory as \"dicom-destination-directory\"\n\n```bash\nmkdir dicom-destination-directory\ncd dicom-destination-directory\nsudo storescp -v --fork -aet MYPACS 1004\n```\n\n`MYPACS` is the Application Entity (AE) title of the external DICOM Receiver.\n\n`1004` is the port number where `storescp` would be listening for any incoming DICOM traffic. Please note that the IP address will be the same as the local system.\n\n#### Setup an External DICOM Sender\n\nYou can use the storescu utility from DCMTK as a DICOM sender.\n\n### Customize the Pipeline\n\nBefore publishing a pipeline, default settings in the pipeline definition YAML file can be modified to configure the resource request and orchestration mode. We will use the `COVID-19-pipeline.yaml` to show the steps.\n\n#### Customize GPU request\n\nThe number of GPU's requested for an operator and/or a pipeline dependent service e.g. TensorRT Inference Server, can be customized if multiple GPU's are available.\n\nThe GPU request for TensorRT Inference Server is set at 1 by default shown below,\n```YAML\n- name: trtis\n # TensorRT Inference Server, required by this AI application.\n container:\n image: nvcr.io/nvidia/tensorrtserver\n tag: 19.08-py3\n command: [\"trtserver\", \"--model-store=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models\"]\n requests:\n gpu: 1\n\n```\nWith this setting, the TensorRT Inference Server can only access one GPU. When more GPU's are available and intended to be used to increase the inference performance, the GPU request number must be customized to match desired number, e.g. 4, as shown below,\n```YAML\n- name: trtis\n # TensorRT Inference Server, required by this AI application.\n container:\n image: nvcr.io/nvidia/tensorrtserver\n tag: 19.08-py3\n command: [\"trtserver\", \"--model-store=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models\"]\n requests:\n gpu: 4\n\n```\n**Note:** If another pipeline with a lesser number of GPU's for TensorRT Inference Server has been published and triggered, the changes made here will not affect the running instance of TensorRT Inference Server.\n\n#### Customize the Orchestration Mode\n\nIn this release of the Clara Deploy Platform, Clara Pipeline Driver [cpd] orchestration is supported, in addition to the Argo based orchestration mode.\n\nThis mode can be customized with the `api-version` attribute in the pipeline definition YAML file, setting the `api-version` to `0.3.0` makes the pipeline run in Argo mode, whereas setting this attribute to `0.4.0` Clara Pipeline Driver mode. The following example from the `COVID-19-pipeline.yaml` specifies Argo mode.\n```YAML\napi-version: 0.3.0\nname: COVID-19-pipeline\n```\n\n### Create the Pipeline\n\nPlease refer to `Setup` section to create this Pipeline.\n\n**Note:** Note down the pipeline ID as it would be used in a later section to define a new pipeline mapping in the DICOM adapter.\n\n### Configure the Clara DICOM Destination\n\nWhen you send images to the Clara Deploy SDK, the DICOM Adapter component acts as the internal DICOM Service Class Provider or a receiving agent.\n\n* Open the file `~/.clara/charts/dicom-adapter/files/dicom-server-config.yaml`.\n* In the dicom->scp->sources section, ensure sender host-ip and ae-titles are correct. Alternatively, set reject-unknown-sources to false, which will allow any DICOM sender to create an association.\n\n ```yaml\n dicom:\n scp:\n reject-unknown-sources: false\n sources:\n - host-ip: 172.22.8.156\n ae-title: DCM4CHEE\n ```\n\n* In the dicom->scp->ae-titles section, add a new ae-title with the name `COVID-19Cls`\n\n ```yaml\n dicom:\n scp:\n ae-titles:\n - ae-title: COVID-19\n ```\n\n**Note** Per the DICOM standards, the length of AE-Title should not exceed 16 characters.\n\n* In the pipeline-mappings section, define a new mapping with: `COVID-19-Class` as the name and `COVID-19` as the `clara-ae-title`, and use the pipeline-id that was created before.\n\n ```yaml\n pipeline-mappings:\n - name: COVID-19-Class\n clara-ae-title: COVID-19\n pipeline-id: [your-pipeline-id-from-above]\n ```\n\n* Update the IP address of the destination with the name `MYPACS` by updating the field host-ip with the IP of your local development machine.\n\n ```yaml\n dicom:\n scu:\n destinations:\n - name: MYPACS\n host-ip: [IP-address-of-remote-PACS]\n port: 1004\n ae-title: MYPACS\n ```\n\n* Restart the DICOM server by issuing the following commands:\n\n```bash\n# Stop DICOM Adapter by running the following command:\nclara dicom stop\n# Run the following command to check whether the DICOM adapter pod is still running:\nkubectl get pods | grep clara-dicom-adapter\n# Once the DICOM adapter pod is fully stopped and no pod is returned with the above command, the DICOM adapter can be started by running the following command:\nclara dicom start\n```\n\n* Verify that the DICOM Adapter is correctly started:\n\n```bash\nhelm ls | grep dicom-adapter\nkubectl get pods | grep dicom-adapter\n```\n\n### Trigger Pipeline\n\nA pipeline is triggered when a DICOM series is sent to the specific AE title of the Clara DICOM Adapter which is associated with that pipeline. To trigger this pipeline, you will need to have a set of relevant DICOM images that can be used as source input data.\n\nFor this pipeline, a sample test dataset in dicom format is available within the package in `/dcm`.\n\n* Open a terminal and go to the directory which contains all your DICOM images.\n\n* Trigger the pipeline (push DICOM data to the proper ae title)\n\n```bash\nstorescu -v +sd +r -xb -aet \"DCM4CHEE\" -aec \"COVID-19\" AA.BB.CC.DD 104 ./\n```\n\nReplace \"AA.BB.CC.DD\" with the IP of your local machine.\n\nWhen the above step is completed, you should see the following result in the terminal\n\n```bash\nI: Received Store Response (Success)
\nI: Releasing Association\n```\n\n### Verify the Pipeline Execution in Dashboard\n\n* Go to the Clara dashboard UI using a web browser:\nThe URL is: `:8000`\n* You should see a job with a name that includes the name of the pipeline you created\n* You can inspect the status of each operator inside that job\n* _Viewing status on a UI for Pipeline Jobs executed using the Clara orchestrator option is currently not available_.\n\n### Verify That the External DICOM Receiver Received Your Images\n\nIf your pipeline outputs DICOM images and sends them to an external DICOM receiver, you may want to verify that the images were indeed received. If you used the `storescp` utility from DCMTK as mentioned earlier, please use a terminal to navigate to the \"dicom-destination-directory\" and verify whether the expected images are stored there. Also, you may want to use a third-party DICOM\nviewer to view those images.","hasQuickStartGuide","advanced","","hasAdvanced","performance","hasPerformance","policies",[],{"_181":198,"_182":199,"_147":-5,"_155":26,"_157":26,"_170":197,"_158":65},{"_181":196,"_182":196,"_147":-5,"_155":26,"_157":26,"_170":197,"_158":65},{"_181":194,"_182":194,"_147":-5,"_155":26,"_157":26,"_170":195,"_158":65},{"_181":192,"_182":192,"_147":-5,"_155":26,"_157":26,"_170":193,"_158":65},{"_181":190,"_182":190,"_147":-5,"_155":26,"_157":26,"_170":191,"_158":65},{"_181":188,"_182":188,"_147":-5,"_155":26,"_157":26,"_170":189,"_158":65},{"_181":186,"_182":186,"_147":-5,"_155":26,"_157":26,"_170":187,"_158":65},{"_181":184,"_182":184,"_147":-5,"_155":26,"_157":26,"_170":185,"_158":65},{"_181":14,"_182":14,"_147":-5,"_155":26,"_157":26,"_170":183,"_158":65},"id","name",[],"0.6.0-2006.3",[],"0.6.0-2006.4",[],"0.7.1-2008.1",[],"0.7.2-2010.1",[],"0.7.3-2011.5",[],"0.7.4-2103.2",[],"0.8.1-2108.1",[],"-","Latest (0.8.1-2108.1)","nvidia/clara/clara_ai_covid19_pipeline","key","nvidia/clara/resources/clara_ai_covid19_pipeline","resourceId","clara_ai_covid19_pipeline","shortDescription","Clara Deploy Clara Deploy AI COVID-19 Classification Pipeline","# Clara Deploy SDK is being consolidated into Clara Holoscan SDK\n\t\nMore info https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/collections/claradeploy\n\t\n\n# Clara Deploy AI COVID-19 Classification Pipeline\n\nCAUTION: This is NOT for diagnostics use.\n\nThis asset requires the Clara Deploy SDK. Follow the instructions on the\n[Clara Ansible](https://ngc.nvidia.com/catalog/model-scripts/nvidia:clara:clara_ansible) page\nto install the Clara Deploy SDK.\n\n## DISCLAIMER\n\nThis inference pipeline was developed by NVIDIA. It is based on a segmentation and classification model developed by NVIDIA researchers in conjunction with the NIH. The Software is for Research Use Only. Software’s recommendation should not be solely or primarily relied upon to diagnose or treat COVID-19 by a Healthcare Professional. This research use only software has not been cleared or approved by FDA or any regulatory agency\n\n---\n\nThis reference pipeline is designed to infer the probability of COVID-19 infection with the chest CT scan of a patient. A 3D lung segmentation model as well as a 3D classification model are used in this pipeline. A single aixial DICOM series of chest CT scan is the pipeline input, and the final output is the report of the probabilities of COVID-19 and non-COVID-19.\n\nOnce the DICOM instances are received, the pipeline is triggered to first convert the DICOM instances to a volume image in [MetaImage format](https://itk.org/Wiki/ITK/MetaIO/Documentation). This image is then used as the input to two segmentation operators concurrently, the lugn segmentation and the COVID-19 lesion segmentation. Each segmentation operator performs inference using its own segmentation AI model, and generates a labeled segmentation as a binary mask on each slice of the volume, with the lung or lung lesion labeled as 1, and the background labeled as 0.\n\nIn the next step, the original volume image along with the labeled segmentation image are used by the COVID-19 classification operator to infer the probabilities of COVID-19 using the COVID-19 classification model.\n\nThe volume images generated by the COVID-19 lesion segmentation operator and the lung segmentation operator are also used to as inputs to a volume ratio metrics operator, which calculates and saves the lesion to lung ratio. \n\nThe pipeline generates the following outputs:\n- Lung segmentation image in [MetaImage format](https://itk.org/Wiki/ITK/MetaIO/Documentation)\n- COVID-19 lesion segmentation image in [MetaImage format](https://itk.org/Wiki/ITK/MetaIO/Documentation)\n- A new DICOM series for the lung segmentation image, optionally sent to a DICOM device\n- A new DICOM series for the COVID-19 lesion segmentation image, optionally sent to a DICOM device \n- A new DICOM series of Encapsulated PDF for the AI classification results, optionally sent to a DICOM device\n- A new DICOM series of Encapsulated PDF for the COVID-19 lesion to lung ratio results, optionally sent to a DICOM device\n- The original and lung segmentation volumes in [MetaImage format](https://itk.org/Wiki/ITK/MetaIO/Documentation) to the Clara Deploy Render Server.\n- The original and COVID-19 lesion segmentation volumes in [MetaImage format](https://itk.org/Wiki/ITK/MetaIO/Documentation) to the Clara Deploy Render Server.\n\n## Pipeline Definition\n\nThis pipeline is defined in the Clara Deploy pipeline definition language. It utilizes built-in reference containers to construct the following set of operators:\n\n- The dicom-reader operator converts input DICOM data into volume images in [MetaImage format](https://itk.org/Wiki/ITK/MetaIO/Documentation) format.\n- The lung segmentation operator performs AI inference against the NVIDIA [Triton Inference Server](https://docs.nvidia.com/deeplearning/sdk/triton-inference-server-guide/docs/index.html) to generate segmentation volume images.\n- The COVID-19 lesion segmentation operator performs AI inference against the NVIDIA [Triton Inference Server](https://docs.nvidia.com/deeplearning/sdk/triton-inference-server-guide/docs/index.html) to generate segmentation volume images.\n- The classification operator performs AI inference against the NVIDIA [Triton Inference Server](https://docs.nvidia.com/deeplearning/sdk/triton-inference-server-guide/docs/index.html) to infer the probability of COVID-19.\n- The COVID-19 lesion to lung volume ratio operator. \n- The dicom-seg-writer converts the lung segmentation volume image into a DICOM SEG instance with a new series instance UID but the same study instance UID of the original DICOM.\n- The register-dicom-output operator registers the lung DICOM SEG instance with the Clara Deploy DICOM Adapter which in turn stores the instance on external DICOM devices per configuration.\n- The dicom-seg-writer for COVID-19 lesion converts the COVID-19 lesion segmentation volume image into a DICOM SEG instance with a new series instance UID but the same study instance UID of the original DICOM.\n- The register-dicom-output operator for COVID-19 lesion registers the lesion DICOM SEG instance with the Clara Deploy DICOM Adapter which in turn stores the instance on external DICOM devices per configuration.\n- The dicom-report-writer converts the AI classification results into DICOM Encapsulated PDF instances with a new series instance UID but the same study instance UID of the original DICOM.\n- The dicom-report-writer for volume ratio metrics converts the COVID-19 lesion to lung volume ratio metrics into a DICOM Encapsulated PDF instances with a new series instance UID but the same study instance UID of the original DICOM.\n- The register-dicom-report operator registers the DICOM Encapsulated PDF with the Clara Deploy DICOM Adapter which in turn stores the instance on external DICOM devices per configuration.\n- The register-volume-images-for-rendering operator registers original and segmented volume images with the Clara Deploy Render Server for visualization.\n\nThe following is the details of pipeline definition, with comments describing each operator's functions\nas well as input and output.\n\n```yaml\napi-version: 0.4.0\nname: COVID-19-pipeline\noperators:\n# dicom reader operator\n# Input: '/input' mapped directly to the input of the pipeline, which is populated by the DICOM Adaptor.\n# Output:'/output' for saving converted volume image in MHD format to file whose name\n# is the same as the DICOM series instance ID.\n- name: dicom-reader\n description: Converts DICOM instances into MHD, one file per DICOM series.\n container:\n image: clara/dicom-reader\n tag: latest\n input:\n - path: /input\n output:\n - path: /output\n# lung-segmentation operator\n# Input: `/input` containing volume image data, MHD format, with a single volume.\n# Output: `/output` containing segmented volume image, MHD format.\n# `/publish` containing original and segmented volume images, MHD format,\n# along with rendering configuration file.\n- name: lung-segmentation\n description: Segmentation of lung using DL trained model.\n container:\n image: clara/ai-lung\n tag: latest\n requests:\n memory: 8192\n input:\n - from: dicom-reader\n path: /input\n output:\n - path: /output\n name: segmentation\n - path: /publish\n name: rendering\n services:\n - name: triton\n # Triton Inference Server, required by this AI application.\n container:\n image: nvcr.io/nvidia/tritonserver\n tag: 20.07-v1-py3\n command: [\"tritonserver\", \"--model-repository=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models\"]\n # services::connections defines how the Triton service is expected to\n # be accessed. Clara Platform supports network (\"http\") and\n # volume (\"file\") connections.\n connections:\n http:\n # The name of the connection is used to populate an environment\n # variable inside the operator's container during execution.\n # This AI application inside the container needs to read this variable to\n # know the IP and port of Triton in order to connect to the service.\n - name: NVIDIA_CLARA_TRTISURI\n port: 8000\n # Some services need a specialized or minimal set of hardware. In this case\n # NVIDIA Triton Inference Server [Triton] requires at least one GPU to function.\n# dicom segmentation writer operator\n# Input1: `/input` containing a volume image file, in MHD format, name matching the DICOM series instance UID.\n# Input2: `/dcm` containing original DICOM instances, i.e. dcm file.\n# Output: `/output` containing the DICOM instances converted from the volume image, with updated attributes\n# based on original DICOM instances.\n- name: dicom-seg-writer\n description: Converts MHD into DICOM instance with attributes based on the original instances.\n container:\n image: clara/dicom-seg-writer\n tag: latest\n variables:\n NVIDIA_SEG_LABELS: \"[\\\"Lung-seg\\\"]\"\n input:\n - from: lung-segmentation\n name: segmentation\n path: /input\n - path: /dcm\n output:\n - path: /output\n name: dicom\n - path: /logs\n name: logs\n# register-volume-images-for-rendering operator\n# Input: Published original and segmented volume images, MHD format, along with rendering configuration file\n# from the segmentation operator.\n# Output: N/A. Input data will be sent to the destination, namely `renderserver` for Render Server DataSet Service.\n- name: register-volume-images-for-rendering\n description: Register volume images, MHD format, for rendering.\n container:\n image: clara/register-results\n tag: latest\n command: [\"python\", \"register.py\", \"--agent\", \"renderserver\"]\n input:\n - from: lung-segmentation\n name: rendering\n path: /input\n# register-dicom-output operator\n# Input: `/input` containing DICOM instances in the named output, `dicom` from dicom-seg-writer operator.\n# Output: N/A. Input data will be sent to the destinations, namely DICOM devices, by the Clara DICOM SCU agent.\n- name: register-dicom-output\n description: Register converted DICOM instances with Results Service to be sent to external DICOM devices.\n container:\n image: clara/register-results\n tag: latest\n command: [\"python\", \"register.py\", \"--agent\", \"ClaraSCU\", \"--data\", \"[\\\"MYPACS\\\"]\"]\n input:\n - from: dicom-seg-writer\n name: dicom\n path: /input\n## Begin: COVID-19 Lesion\n# covid-lesion-segmentation operator\n# Input: `/input` containing volume image data, MHD format, with a single volume.\n# Output: `/output` containing segmented volume image, MHD format.\n# `/publish` containing original and segmented volume images, MHD format,\n# along with rendering configuration file.\n- name: covid-lesion-segmentation\n description: Segmentation of COVID-19 GGO using DL trained model.\n container:\n image: clara/ai-covid-lesion\n tag: latest\n requests:\n memory: 8192\n input:\n - from: dicom-reader\n path: /input\n output:\n - path: /output\n name: segmentation\n - path: /publish\n name: rendering\n services:\n - name: triton\n # Triton Inference Server, required by this AI application.\n container:\n image: nvcr.io/nvidia/tritonserver\n tag: 20.07-v1-py3\n command: [\"tritonserver\", \"--model-repository=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models\"]\n # services::connections defines how the Triton service is expected to\n # be accessed. Clara Platform supports network (\"http\") and\n # volume (\"file\") connections.\n connections:\n http:\n # The name of the connection is used to populate an environment\n # variable inside the operator's container during execution.\n # This AI application inside the container needs to read this variable to\n # know the IP and port of Triton in order to connect to the service.\n - name: NVIDIA_CLARA_TRTISURI\n port: 8000\n # Some services need a specialized or minimal set of hardware. In this case\n # NVIDIA Triton Inference Server [Triton] requires at least one GPU to function.\n# dicom covid-lesion segmentation writer operator\n# Input1: `/input` containing a volume image file, in MHD format, name matching the DICOM series instance UID.\n# Input2: `/dcm` containing original DICOM instances, i.e. dcm file.\n# Output: `/output` containing the DICOM instances converted from the volume image, with updated attributes\n# based on original DICOM instances.\n- name: dicom-seg-writer-covid-lesion\n description: Converts MHD into DICOM instance with attributes based on the original instances.\n container:\n image: clara/dicom-seg-writer\n tag: latest\n variables:\n NVIDIA_SEG_LABELS: \"[\\\"GroundGlass-seg\\\"]\"\n input:\n - from: covid-lesion-segmentation\n name: segmentation\n path: /input\n - path: /dcm\n output:\n - path: /output\n name: dicom\n - path: /logs\n name: logs\n# register-volume-images-for-rendering operator\n# Input: Published original and segmented volume images, MHD format, along with rendering configuration file\n# from the segmentation operator.\n# Output: N/A. Input data will be sent to the destination, namely `renderserver` for Render Server DataSet Service.\n- name: register-covid-lesion-volume-images-for-rendering\n description: Register volume images, MHD format, for rendering.\n container:\n image: clara/register-results\n tag: latest\n command: [\"python\", \"register.py\", \"--agent\", \"renderserver\"]\n input:\n - from: covid-lesion-segmentation\n name: rendering\n path: /input\n# register-dicom-output operator\n# Input: `/input` containing DICOM instances in the named output, `dicom` from dicom-seg-writer operator.\n# Output: N/A. Input data will be sent to the destinations, namely DICOM devices, by the Clara DICOM SCU agent.\n- name: register-covid-lesion-dicom-output\n description: Register converted DICOM instances with Results Service to be sent to external DICOM devices.\n container:\n image: clara/register-results\n tag: latest\n command: [\"python\", \"register.py\", \"--agent\", \"ClaraSCU\", \"--data\", \"[\\\"MYPACS\\\"]\"]\n input:\n - from: dicom-seg-writer-covid-lesion\n name: dicom\n path: /input\n## END: COVID-19 lesion segmentation\n# COVID-19 Classification operator\n# Input: original image (DICOM series converted image) and segmented volume images, MHD format.\n# Output: CSV file for classification resuls: the probabilities for both COVID-19 and non-COVID-19.\n- name: classification-covid-19\n description: Classification of COVID-19 using DL model with original and segmentation images.\n container:\n image: clara/ai-covid-19\n tag: latest\n requests:\n memory: 4096\n input:\n - from: lung-segmentation\n name: segmentation\n path: /label_image\n - from: dicom-reader\n path: /input\n output:\n - path: /output\n name: classification\n services:\n - name: triton\n # Triton Inference Server, required by this AI application.\n container:\n image: nvcr.io/nvidia/tritonserver\n tag: 20.07-v1-py3\n command: [\"tritonserver\", \"--model-repository=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models\"]\n # services::connections defines how the Triton service is expected to\n # be accessed. Clara Platform supports network (\"http\") and\n # volume (\"file\") connections.\n connections:\n http:\n # The name of the connection is used to populate an environment\n # variable inside the operator's container during execution.\n # This AI application inside the container needs to read this variable to\n # know the IP and port of Triton in order to connect to the service.\n - name: NVIDIA_CLARA_TRTISURI\n port: 8000\n # Some services need a specialized or minimal set of hardware. In this case\n # NVIDIA Triton Inference Server [Triton] requires at least one GPU to function.\n# dicom report writer operator\n# Input1: `/input` containing a classification result file, in txt or csv format.\n# Input2: `/dcm` containing original DICOM instances, i.e. dcm file.\n# Output: `/output` containing the DICOM report instances converted with attributes\n# from the original DICOM instances.\n- name: dicom-report-writer\n description: Create DICOM AI Results report, with attributes based on the original instances.\n container:\n image: clara/dicom-report-writer\n tag: latest\n variables:\n NVIDIA_DICOM_REPORT_TYPE: 'pdf'\n NVIDIA_AI_MODEL_CREATOR: 'NVIDIA/NIH'\n NVIDIA_AI_MODEL_NAME: 'Clara CT Classification COVID-19'\n NVIDIA_AI_MODEL_VERSION: '1.0'\n input:\n - from: classification-covid-19\n name: classification\n path: /input\n - path: /dcm\n output:\n - path: /output\n name: dicom\n - path: /logs\n name: logs\n# register-dicom-report operator\n# Input: `/input` containing DICOM instances in the named output, `dicom` from dicom-seg-writer operator.\n# Output: N/A. Input data will be sent to the destinations, namely DICOM devices, by the Clara DICOM SCU agent.\n- name: register-dicom-report\n description: Register DICOM instances with Results Service to be sent to external DICOM devices.\n container:\n image: clara/register-results\n tag: latest\n command: [\"python\", \"register.py\", \"--agent\", \"ClaraSCU\", \"--data\", \"[\\\"MYPACS\\\"]\"]\n input:\n - from: dicom-report-writer\n name: dicom\n path: /input\n## Begin: covid_lung_ratio\n# COVID-19 Classification operator\n# Input: COVID-19 lesion volume image and segmented lung volume images, MHD format.\n# Output: txt file for volume ratio metrics.\n- name: covid-lung-volume-ratio\n description: Classification of COVID-19 using DL model with original and segmentation images.\n container:\n image: clara/ai-volume-ratio\n tag: latest\n requests:\n memory: 2048\n input:\n - from: lung-segmentation\n name: segmentation\n path: /label_image\n - from: covid-lesion-segmentation\n name: segmentation\n path: /input\n output:\n - path: /output\n name: metrics\n# dicom report writer operator\n# Input1: `/input` containing a classification result file, in txt or csv format.\n# Input2: `/dcm` containing original DICOM instances, i.e. dcm file.\n# Output: `/output` containing the DICOM report instances converted with attributes\n# from the original DICOM instances.\n- name: dicom-report-writer-metrics\n description: Create DICOM AI Results report, with attributes based on the original instances.\n container:\n image: clara/dicom-report-writer\n tag: latest\n variables:\n NVIDIA_DICOM_REPORT_TYPE: 'pdf'\n NVIDIA_AI_MODEL_CREATOR: 'NVIDIA/NIH'\n NVIDIA_AI_MODEL_NAME: 'Clara COVID-19 Lung Lesion'\n NVIDIA_AI_MODEL_VERSION: '1.0'\n input:\n - from: covid-lung-volume-ratio\n name: metrics\n path: /input\n - path: /dcm\n output:\n - path: /output\n name: dicom\n - path: /logs\n name: logs\n# register-dicom-report operator\n# Input: `/input` containing DICOM instances in the named output, `dicom` from dicom-seg-writer operator.\n# Output: N/A. Input data will be sent to the destinations, namely DICOM devices, by the Clara DICOM SCU agent.\n- name: register-dicom-report-metrics\n description: Register DICOM instances with Results Service to be sent to external DICOM devices.\n container:\n image: clara/register-results\n tag: latest\n command: [\"python\", \"register.py\", \"--agent\", \"ClaraSCU\", \"--data\", \"[\\\"MYPACS\\\"]\"]\n input:\n - from: dicom-report-writer-metrics\n name: dicom\n path: /input\n## End covid_lung_ratio\n```\n\n## Executing the Pipeline\n\nPlease refer to the `Quick Start Guide` section on how to register a pipeline, configure the DICOM Adapter, and execute the pipeline.\n\n## License\n\nAn [End User License Agreement](https://developer.nvidia.com/nvidia-clara-sdk-license) is included with the product. By pulling and using the Clara Deploy asset on NGC, you accept the terms and conditions of these licenses. For Clara Deploy AI COVID-19 Classification Pipeline you \naccept the terms and conditions that are mentioned in the license file inside the package.\n\n## Suggested Reading\n\nRelease Notes, the Getting Started Guide, and the SDK itself are available at the [NVIDIA Developer forum](https://developer.nvidia.com/clara).\n\nFor answers to any questions you may have about this release, visit the [NVIDIA Devtalk forum](https://forums.developer.nvidia.com/healthcare/clara-deploy-sdk).","displayName","Clara Deploy AI COVID-19 Classification Pipeline [Deprecated]",["D",1680632505749],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/clara/resources/clara_ai_covid19_pipeline","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Clara.png","canGuestDownload","latestVersionSizeInBytes",418179484,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[241,242,243,244,245,246],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_208":268,"_248":268,"_249":269,"_251":252},{"_208":266,"_248":266,"_249":267,"_251":252},{"_208":263,"_248":264,"_249":265,"_251":252},{"_208":259,"_248":260,"_249":261,"_251":252},{"_208":255,"_248":256,"_249":257,"_251":252},{"_208":247,"_248":247,"_249":250,"_251":252},"TensorFlow","value","filter",{"_253":254,"_248":247},"category","label","field","framework","Inference","soln_inference",{"_253":258,"_248":256},"technology","Healthcare","indus_healthcare",{"_253":262,"_248":260},"industry","DL","soln_dl",{"_253":258,"_248":264},"clara4covid-19",{"_253":252,"_248":266},"Clara Deploy",{"_253":252,"_248":268},{"_86":1248,"_88":1249},"ngcApps",[1201],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1182":1183,"_1184":1185,"_1186":1187,"_1188":1189,"_1190":281,"_117":307,"_1191":1192,"_1193":50,"_1194":65,"_1195":65,"_1196":1197,"_1198":65,"_1199":1200},"amplitude",{"_1179":1180,"_820":-7,"_1181":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1173":1174,"_1175":1176,"_1177":1178},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787789697541,"1.12.0","defaultSearchSuggestions",{"_1060":166,"_1061":1062,"_1063":1064,"_1065":1066},"announcements",[],"consentState",{"_345":1050,"_1051":65,"_1052":26,"_1053":26,"_1054":1055},"consentConfig",{"_327":328,"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346},"showContactAdmin",1787789697543,[],[],[321,322,323,324,325,326],{"_39":54,"_41":63,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":61,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":59,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":54,"_41":55,"_43":81,"_45":-5,"_47":-5,"_49":50},{"_39":51,"_41":52,"_43":44,"_45":46,"_47":48,"_49":50},{"_39":40,"_41":42,"_43":81,"_45":-5,"_47":-5,"_49":50},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. 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It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",4,[576,577,578,579,580,581,582,583,584,585,586,587,588,589,590,591,592,593,594,595,596,597,598,599,600],[547,548,549,550,551],{"_385":460,"_208":460,"_144":166,"_359":571},{"_385":434,"_208":434,"_144":166,"_359":566},{"_385":561,"_208":561,"_144":166,"_359":562},{"_385":556,"_208":556,"_144":166,"_359":557},{"_385":552,"_208":552,"_144":166,"_359":553},"nvidia.partners",[554],{"_182":555,"_391":552,"_392":26,"_144":428},"edge","app.hushly.com",[558],{"_182":559,"_391":556,"_392":26,"_144":560},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[563],{"_182":564,"_391":561,"_392":26,"_144":565},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[659,660,661,662,663,664,665,666,667,668,669,670,671,672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687],[639,640,641,642],{"_385":654,"_208":654,"_144":166,"_359":655},{"_385":649,"_208":649,"_144":166,"_359":650},{"_385":447,"_208":447,"_144":166,"_359":646},{"_385":561,"_208":561,"_144":166,"_359":643},[644],{"_182":645,"_391":561,"_392":65,"_144":565},"_gh_sess",[647],{"_182":648,"_391":447,"_392":65,"_144":454},"YSC","bizible.com",[651],{"_182":652,"_391":649,"_392":26,"_144":653},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[656],{"_182":657,"_391":654,"_392":26,"_144":658},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_182":729,"_391":506,"_392":26,"_144":730},{"_182":714,"_391":728,"_392":65,"_144":715},{"_182":697,"_391":509,"_392":26,"_144":622},{"_182":697,"_391":610,"_392":26,"_144":622},{"_182":726,"_391":506,"_392":26,"_144":727},{"_182":714,"_391":512,"_392":65,"_144":715},{"_182":724,"_391":506,"_392":26,"_144":725},{"_182":714,"_391":509,"_392":65,"_144":715},{"_182":722,"_391":609,"_392":26,"_144":723},{"_182":714,"_391":631,"_392":65,"_144":715},{"_182":694,"_391":509,"_392":26,"_144":696},{"_182":720,"_391":506,"_392":65,"_144":721},{"_182":718,"_391":506,"_392":26,"_144":719},{"_182":716,"_391":506,"_392":65,"_144":717},{"_182":714,"_391":605,"_392":65,"_144":715},{"_182":712,"_391":506,"_392":26,"_144":713},{"_182":710,"_391":506,"_392":26,"_144":711},{"_182":708,"_391":506,"_392":65,"_144":709},{"_182":706,"_391":614,"_392":26,"_144":707},{"_182":694,"_391":605,"_392":26,"_144":696},{"_182":704,"_391":506,"_392":26,"_144":705},{"_182":702,"_391":506,"_392":65,"_144":703},{"_182":700,"_391":506,"_392":26,"_144":701},{"_182":698,"_391":506,"_392":26,"_144":699},{"_182":697,"_391":506,"_392":26,"_144":622},{"_182":694,"_391":695,"_392":26,"_144":696},{"_182":692,"_391":506,"_392":26,"_144":693},{"_182":690,"_391":506,"_392":65,"_144":691},{"_182":688,"_391":506,"_392":26,"_144":689},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. 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