10.32. Digital Pathology Nuclei Segmentation Operator

Digital Pathology Nuclei Segmentation Operator is a reference application that makes use of Clara Pipeline Driver and OpenSlide for Digital Pathology image segmentation (cell nuclei setmentation).

This application, in the form of a Docker container, is expected to work with the Clara (CPDriver) orchestrator engine to use FastIO features, but it can work as a standalone application with Docker if the environment variable NVIDIA_CLARA_NOSYNCLOCK is set to TRUE.

The Digital Pathology Nuclei Segmentation Operator uses the following model/packages:

The main code is available at /app/main.py, and it is executed with parameters inside the container, as shown below:


/bin/bash -c 'python -u /app/main.py <command name>'


usage: main.py [-h] [-d DEBUG_LEVEL] [--input-path INPUT_PATH] [--output-path OUTPUT_PATH] [--config-path CONFIG_PATH] [-w NUM_WORKERS] [--mask-pixel-count-limit MASK_PIXEL_COUNT_LIMIT] [-t TILE_SIZE] [-m MODEL_NAME] [-o OVERLAP] command positional arguments: command Command to execute optional arguments: -h, --help show this help message and exit -d DEBUG_LEVEL, --debug-level DEBUG_LEVEL Set debug level (e.g., 'INFO', 'DEBUG') --input-path INPUT_PATH Input folder path. Default is '/input' --output-path OUTPUT_PATH Output folder path. Default is '/output' --config-path CONFIG_PATH Config folder path. Default is '/config' -w NUM_WORKERS, --num-workers NUM_WORKERS Number of workers. Default is (# of cpus) --mask-pixel-count-limit MASK_PIXEL_COUNT_LIMIT Mask pixel count limit. Default is 1024 * 1024 -t TILE_SIZE, --tile-size TILE_SIZE Tile size. Default is 256 -m MODEL_NAME, --model-name MODEL_NAME Model name. Default is 'segmentation_unet_nuclei' -o OVERLAP, --overlap OVERLAP Overlap size. Default is 0. Not used for now

According to the <command name>, it does a different job and each command acts as a stage in the pipeline. segmentation

This executes all the operations (load/filter/stitch) at once.

This command loads a multi-res SVS file, tiles it, performs inferences with TRITON, and then writes out the multi-resolution/tiled image into the file system. This process consists of the following three stages:

  • Pre-processing: It loads a whole slide image at a low-resolution to generate a mask. The generated mask image is used to skip inferencing on background tiles. For each tile, some filters (color conversion, normalization, and so on) are applied before inferencing.
  • Inferencing: For each tile (256x256x3, uint8), it uses TRITON-based inference to segment nuclei in the tile.
  • Post-processing: For each segmentation result in the tile, the segmentation part is overlaid on top of the original image. Each post-processed tile is saved into a single multi-resolution/tiled TIFF file using the Tifffile library. Input

Input requires a folder (mounted at the /input folder inside the container) containing the following files:

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

This command expects that a TRITON server is running and the server HTTP API address is available through the environment variable NVIDIA_CLARA_TRTISURI (e.g., ‘’) so that inference calls are done on a model specified by the model name parameter (-m or --model-name). Output

The following files are be stored at /output folder inside the container:

  • image.tif: Output image file
  • config.meta: Metadata for Render Server
  • config_render.json: Configuration for Render Server

© Copyright 2018-2020, NVIDIA Corporation. All rights reserved. Last updated on Jun 28, 2023.