NGC | Catalog
CatalogResourcesClara Deploy Digital Pathology Nuclei Segmentation Pipeline

Clara Deploy Digital Pathology Nuclei Segmentation Pipeline

For downloads and more information, please view on a desktop device.
Logo for Clara Deploy Digital Pathology Nuclei Segmentation Pipeline


Clara Deploy Digital Pathology Nuclei Segmentation Pipeline [Deprecated]



Latest Version



April 4, 2023

Compressed Size

57.55 MB

Clara Deploy SDK is being consolidated into Clara Holoscan SDK

More info

Digital Pathology Nuclei Segmentation Pipeline

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


The Digital Pathology Nuclei Segmentation pipeline is one of the reference pipelines provided with Clara Deploy SDK. It accepts an image in formats that are readable by OpenSlide format. The output is a color image where cell nuclei segmentation results are overlaid on top of the original image. The result image is published to the Render Server so that it can be viewed on the web browser.

Pipeline Definition

The Digital Pathology Nuclei Segmentation pipeline is defined in the Clara Deploy pipeline definition language. This pipeline utilizes built-in reference containers to construct the following operator:

The followings are pipeline definitions available:


api-version: 0.4.0
name: dp-nuclei-segmentation-pipeline
  NUM_WORKERS: -1              # -1: # of cpus
  - name: segmentation
    description: Do cell nuclei segmentation
      image: clara/dp-nuclei-seg
      tag: latest
      command: ["/bin/bash", "-c", "python -u /app/ segmentation_skimage --num-workers=${{NUM_WORKERS}}"]
      memory: 8192
    - path: /input
    - path: /config
    - path: /output
      - name: triton
        # TRITON inference server, required by this AI application.
          tag: 20.07-v1-py3
          command: ["tritonserver", "--model-repository=$(NVIDIA_CLARA_SERVICE_DATA_PATH)/models"]
        # services::connections defines how the TRITON 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 TRITON in order to connect to the service.
          - name: NVIDIA_CLARA_TRTISURI
            port: 8000
          # Some services need a specialized or minimal set of hardware. In this case
          # NVIDIA TRITON inference server requires at least one GPU to function.
  - name: register-images-for-rendering
    description: Register pyramid images in tiff format for rendering.
      image: clara/register-results
      tag: latest
      command: ["python", "", "--agent", "renderserver"]
    - from: segmentation
      path: /input

The parameter NUM_WORKERS is for setting the number of workers in the pipeline.

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:

  • .tif or .svs - Input image file
  • config_render.json - Configuration for Render Server

Bundled input data in this pipeline is a breast cancer case from The Cancer Genome Atlas.

Data Output

An RGB image where the segmentation part is overlaid on top of the original image, shown on Render Server.


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: (

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