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Clara Deploy AI Pancreas Tumor Segmentation Pipeline

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[Deprecated] Clara Deploy AI Pancreas Tumor Segmentation Pipeline
Latest Version
April 4, 2023
Compressed Size
274.67 MB

Clara Deploy SDK is being consolidated into Clara Holoscan SDK

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Clara AI Pancreas Tumor Segmentation Pipeline

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


The Pancreas Tumor Segmentation pipeline is one of the reference pipelines provided with Clara Deploy SDK. A pre-trained model for volumetric (3D) segmentation of the pancreas from a mono-modal CT image is used in the pipeline. This pipeline depends on the Clara Deploy DICOM Adapter to receive DICOM images, specifically single-channel 3D CT images. Once the DICOM instances are stored, the pipeline is started to first convert the DICOM instances to a volume image in MetaIO format and then processes the input volume image with the Pancreas Tumor Segmentation AI model. The AI model generates the labeled segmentations (within the pancreas) as a mask on slices of the volume of the same size as the input. The pancreas is labeled as 1, pancreas tumor is labeled as 2, and the background is labeled as 0. In its final steps, the pipeline outputs the mask as a new DICOM series using the original study instance UID; also, it outputs the original and segmented volumes to the Clara Deploy Render Server for visualization on the Clara Dashboard.

Pipeline Definition

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

  • The dicom-reader operator converts input DICOM data into volume images in mhd format.
  • The pancreas-tumor-segmentation operator performs AI inference against the NVIDIA Triton Inference server to generate pancreas tumor segmentation volume images.
  • The dicom-writer converts the segmented volume image into DICOM instances with a new series instance UID and the original DICOM study instance UID.
  • 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.
  • 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 as well as input and output.

api-version: 0.4.0
name: pancreas-tumor-pipeline
# dicom reader operator
# Input: '/input' mapped directly to the input of the pipeline, which is populated by the DICOM Adaptor.
# Output:'/output' for saving converted volume image in MHD format to file whose name
#            is the same as the DICOM series instance ID.
- name: dicom-reader
  description: Converts DICOM instances into MHD, one file per DICOM series.
    image: clara/dicom-reader
    tag: latest
  - path: /input
  - path: /output
# pancreas-tumor-segmentation operator
# Input: `/input` containing volume image data, MHD format, with a single volume.
# Output: `/output` containing segmented volume image, MHD format.
#         `/publish` containing original and segmented volume images, MHD format,
#             along with rendering configuration file.
- name: pancreas-tumor-segmentation
  description: Segmentation of pancreas tumor inferencing using DL trained model.
    image: clara/ai-pancreastumor
    tag: latest
    gpu: 1
    memory: 8192
  - from: dicom-reader
    path: /input
  - path: /output
    name: segmentation
  - path: /publish
    name: rendering
  - name: trtis
  # Triton Inference Server, required by this AI application.
      tag: 20.07-v1-py3
      command: ["tritonserver", "--model-repository=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models"]
    # services::connections defines how the TRTIS service is expected to
    # be accessed. Clara Platform supports network ("http") and
    # volume ("file") connections.
      # The name of the connection is used to populate an environment
      # variable inside the operator's container during execution.
      # This AI application inside the container needs to read this variable to
      # know the IP and port of TRTIS in order to connect to the service.
        port: 8000
      # Some services need a specialized or minimal set of hardware. In this case
      # NVIDIA Tensor RT Inference Server [TRTIS] requires at least one GPU to function.
# dicom writer operator
# Input1: `/input` containing a volume image file, in MHD format, name matching the DICOM series instance UID.
# Input2: `/dicom` containing original DICOM instances, i.e. dcm file.
# Output: `/output` containing the DICOM instances converted from the volume image, with updated attributes
#         based on original DICOM instances.
- name: dicom-writer
  description: Converts MHD into DICOM instance with attributes based on the original instances.
    image: clara/dicom-writer
    tag: latest
  - from: pancreas-tumor-segmentation
    name: segmentation
    path: /input
  - path: /dicom
  - path: /output
    name: dicom
# register-volume-images-for-rendering operator
# Input: Published original and segmented volume images, MHD format, along with rendering configuration file
#        from pancreas-tumor-segmentation operator.
# Output: N/A. Input data will be sent to the destination, namely `renderserver` for Render Server DataSet Service.
- name: register-volume-images-for-rendering
  description: Register volume images, MHD format, for rendering.
    image: clara/register-results
    tag: latest
    command: ["python", "", "--agent", "renderserver"]
  - from: pancreas-tumor-segmentation
    name: rendering
    path: /input
# register-dicom-output operator
# Input: `/input` containing DICOM instances in the named output, `dicom` from dicom-writer operator.
# Output: N/A. Input data will be sent to the destinations, namely DICOM devices, by the Clara DICOM SCU agent.
- name: register-dicom-output
  description: Register converted DICOM instances with Results Service to be sent to external DICOM devices.
    image: clara/register-results
    tag: latest
    command: ["python", "", "--agent", "ClaraSCU", "--data", "[\"MYPACS\"]"]
  - from: dicom-writer
    name: dicom
    path: /input

Executing the Pipeline

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


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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: (