9.1. Liver Segmentation Pipeline

The Liver Segmentation pipeline is one of the reference pipelines provided with Clara Deploy SDK. This pipeline depends in the Clara Deploy DICOM Adapter to receive DICOM images, specifically an axial series of an abdominal CT study. 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 Liver Segmentation AI model. The AI model generates the labeled liver and tumors (within the liver) as a binary mask on slices of the volume of the same size as the input. The liver is labeled as 1, tumors within the liver are labeled as 2, and the background is labeled as 0. In its file steps, the pipeline outputs the binary mask as a new DICOM series using the original study instance UID; in addition, it outputs the original and segmented volumes to the Clara Deploy Render Server for visualization on the Clara Dashboard.

9.1.1. Pipeline Definition

The Liver Segmentation pipeline is defined in the Clara Deploy pipeline definition language. This pipeline utilizes the 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 liver-tumor-segmentation operator performs AI inference against the NVIDIA TensorRT Inference server to generate liver and 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 register 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: 0.3.0
name: liver-tumor-pipeline
operators:
# dicom reader operator
# Input: '/input' mapped directly to the input of the pipeline, which is populated by the DICOM Adapter.
# 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.
  container:
    image: clara/dicom-reader
    tag: 0.2.0
  input:
  - path: /input
  output:
  - path: /output
# liver-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: liver-tumor-segmentation
  description: Segmentation of liver and tumor inferencing using DL trained model.
  container:
    image: clara/ai-livertumor
    tag: 0.2.0
  input:
  - from: dicom-reader
    path: /input
  output:
  - path: /output
    name: segmentation
  - path: /publish
    name: rendering
  services:
  - name: trtis
  # TensorRT Inference Server, required by this AI application.
    container:
      image: clara/trtis
      tag: 0.2.0
      command: ["trtserver", "--model-store=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models"]
    # services::connections defines how the TRTIS service is expected to
    # be accessed.
    connections:
      http:
      # 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.
      - name: NVIDIA_CLARA_TRTISURI
        port: 8000
# 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.
  container:
    image: clara/dicom-writer
    tag: 0.2.0
  input:
  - from: liver-tumor-segmentation
    name: segmentation
    path: /input
  - path: /dicom
  output:
  - 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 liver-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.
  container:
    image: clara/register-results
    tag: 0.2.0
    command: ["python", "register.py", "--agent", "renderserver"]
  input:
  - from: liver-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.
  container:
    image: clara/register-results
    tag: 0.2.0
    command: ["python", "register.py", "--agent", "ClaraSCU", "--data", "[\"MYPACS\"]"]
  input:
  - from: dicom-writer
    name: dicom
    path: /input

9.1.2. Executing the Pipeline

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