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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.

Overview

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:

Digital Pathology Image Processing Operator to generate a segmentation image. It uses the following model/packages:
- An open-sourced implementation of nuclei segmentation model from the Kaggle 2018 Data Science Bowl (https://github.com/limingwu8/UNet-pytorch)
- OpenSlide (https://openslide.org/)
- Tifffile (https://github.com/cgohlke/tifffile)
- Scikit-image (https://scikit-image.org/)
- NumPy (https://numpy.org/)
- OpenCV (https://opencv.org/)
Register Results Operator to publish the volume image to Render Server

The followings are pipeline definitions available:

dp-nuclei-segmentation-pipeline.yaml

api-version: 0.4.0
name: dp-nuclei-segmentation-pipeline
parameters:
  NUM_WORKERS: -1              # -1: # of cpus
operators:
  - name: segmentation
    description: Do cell nuclei segmentation
    container:
      image: clara/dp-nuclei-seg
      tag: latest
      command: ["/bin/bash", "-c", "python -u /app/main.py segmentation_skimage --num-workers=${{NUM_WORKERS}}"]
    requests:
      memory: 8192
    input:
    - path: /input
    - path: /config
    output:
    - path: /output
    services:
      - name: triton
        # TRITON inference server, required by this AI application.
        container:
          image: nvcr.io/nvidia/tritonserver
          tag: 20.07-v1-py3
          command: ["tritonserver", "--model-repository=$(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.
        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 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.
    container:
      image: clara/register-results
      tag: latest
      command: ["python", "register.py", "--agent", "renderserver"]
    input:
    - 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.

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.

Clara Deploy Digital Pathology Nuclei Segmentation Pipeline