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3D Image Processing Pipeline using FastIO

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

Overview

The 3D Image Processing pipeline is one of the reference pipelines provided with Clara Deploy SDK. It accepts a volume image in MetaImage format, and optionally accept parameters for cropping. The output is the cropped volume image and the image is published to the Render Server so that it can be viewed on the web browser.

Pipeline Definition

The 3D Image Processing pipeline using FastIO is defined in the Clara Deploy pipeline definition language. This pipeline utilizes built-in reference containers to construct the following operator:

FastIO-ITK Operator to read/filter/write volume image using FastIO and ITK
Register Results Operator to publish the volume image to Render Server

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: fastio-itk-pipeline
parameters:
  CROP_START_X: -1
  CROP_START_Y: -1
  CROP_START_Z: -1
  CROP_SIZE_X: -1
  CROP_SIZE_Y: -1
  CROP_SIZE_Z: -1
operators:
  - name: read-volume
    description: Read a volume file into the shared memory
    container:
      image: clara/fastio-itk
      tag: latest
      command: ["/bin/bash", "-c", "python -u /app/main.py read_volume"]
    input:
    - path: /input
    output:
    - path: /output
    requests:
      memory: 8192
  - name: filter-volume
    description: Read a volume from the shared memory and pass to filters
    container:
      image: clara/fastio-itk
      tag: latest
      command: ["/bin/bash", "-c", "python -u /app/main.py filter_volume"]
    variables:
      CROP_START_X: ${{CROP_START_X}}
      CROP_START_Y: ${{CROP_START_Y}}
      CROP_START_Z: ${{CROP_START_Z}}
      CROP_SIZE_X: ${{CROP_SIZE_X}}
      CROP_SIZE_Y: ${{CROP_SIZE_Y}}
      CROP_SIZE_Z: ${{CROP_SIZE_Z}}
    input:
    - from: read-volume
      path: /input
    output:
    - path: /output
    requests:
      memory: 8192
  - name: write-volume
    description: Write the volume from the shared memory to the file
    container:
      image: clara/fastio-itk
      tag: latest
      command: ["/bin/bash", "-c", "python -u /app/main.py write_volume"]
    input:
    - from: filter-volume
      path: /input
    - path: /config  # user-provided input payload including config_render.json file
    output:
    - path: /output
    requests:
      memory: 8192
  - name: register-volume-images-for-rendering
    description: Register volume images, MHD format, for rendering.
    container:
      image: clara/register-results
      tag: latest
      command: ["python", "register.py", "--agent", "renderserver"]
    input:
    - from: write-volume
      path: /input

Executing the Pipeline

Please refer to the Quick Start Guide section on how to run this reference pipeline using local input files.

Data Input

Input requires a folder containing the following files:

.mhd (with .raw) - Input MetaIO file
config_render.json - Configuration for Render Server

Data Output

Cropped volume on Render Server

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.