Clara Segmentation Contour Detection Operator

This application is NOT for medical use.

This application calculates the contours of a segmentation mask image in MetaImage format and saves the contour points as a nested list in a JSON file. It supports a single label mask in the segmentation image.

This application, in the form of a Docker container, expects an input folder (/input by default), which must be mapped to a host folder. The following file is expected in the folder:

• An image file in MetaImage format containing the segmentation mask with a single label

This application saves a nested list in JSON format to the output folder (/output by default). The output filename is the same as the input filename, but with the .json extension. The /output folder must be mapped to a host folder.

The following is the schema of the nested list:

• A list of items, with each item containing a list of contours for the corresponding slice in the segmentation image. If there is no contour points on a slice, the item is still present but empty.
• A list of contours on a specific slice. The list is zero length if there are no contour points.
• A list of points for each contour, with each point being a list of three floats representing 3D coordinates in the coordinate system defined in the segmentation image.

The following is a condensed example with 5 slices in the image:

            

            
[
    [],
    [
        [
            [
                -21.628832700592994,
                8.208444049987097,
                -34.900416875535804
            ],
            [
                -21.628832700449735,
                8.758731224603924,
                -35.139548751417
            ]
        ]
    ],
    [
        [
            [
                -21.637283139498905,
                3.3509285999774914,
                -29.518566976497958
            ],
            [
                -22.237280782818715,
                3.9005421788187995,
                -29.75924892261019
            ],
            [
                -22.837278426281785,
                3.899868583043281,
                -29.76079899284123
            ]
        ]
    ],
    [],
    []
]
        

Logs generated by the application are saved in a folder (/logs by default), which must be mapped to a host folder.

The directories in the container are shown below. The core of the application code is under the folder contour_detection.

            

            
/app_contour_detection
├── buildContainers.sh
├── contour_detection
│   ├── app.py
│   ├── contour_detector.py
│   ├── __init__.py
│   └── runtime_envs.py
├── Dockerfile
├── __init__.py
├── logging_config.json
├── main.py
├── ngc
│   ├── metadata.json
│   └── overview.md
├── public
│   └── docs
│       └── README.md
├── requirements.txt
├── run_app_docker.sh
└── test-data
    └── mhd
        ├── 1.2.826.0.1.3680043.2.1125.1.48532560258338587890405155270906492.output.mhd
        └── 1.2.826.0.1.3680043.2.1125.1.48532560258338587890405155270906492.output.raw
        

If you want to see the internals of the container and manually run the application, follow these steps:

1. Start the container in interactive mode. See the next section on how to run the container, and replace the docker run command with docker run --entrypoint /bin/bash.
2. Once in the the Docker terminal, ensure the current directory is app_contour_detection.
3. Copy test-data/input/mhd/* to /input.
4. Create /output and /logs folders.
5. Enter the command python ./main.py".
6. Once finished, type exit.

Prerequisites

• The segmentation mask image file in MetaImage format
• The original dcm files from the single DICOM series used for segmentation

Step 1

Change to your working directory (e.g. my_test).

Step 2

Create, if they do not exist, the following directories under your working directory:

• input, and copy over the segmentation mask image file.
• output for the generated DICOM RTSTRUCT dcm file.
• logs for log files.

Step 3

In your working directory, create a shell script (e.g. run_app_docker.sh or other name if you prefer), copy the sample content below, save it, and ensure the TAG variable has the same value as the actual container tag:

            

            
SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
TESTDATA_DIR=$(readlink -f "${SCRIPT_DIR}"/test-data)
APP_NAME="app_contour_detection"
INPUT_TYPE="mhd"

# Build Dockerfile
docker build -t ${APP_NAME} -f ${SCRIPT_DIR}/Dockerfile ${SCRIPT_DIR}

# Run ${APP_NAME} container.
docker run --name ${APP_NAME} -t --rm \
    -v ${TESTDATA_DIR}/${INPUT_TYPE}:/input \
    -v ${SCRIPT_DIR}/output:/output \
    -v ${SCRIPT_DIR}/logs:/logs \
    -e DEBUG_VSCODE \
    -e DEBUG_VSCODE_PORT \
    -e NVIDIA_CLARA_NOSYNCLOCK=TRUE \
    ${APP_NAME}

echo "${APP_NAME}has finished."
        

Step 4

Execute the script as shown below and wait for the application container to finish:

./run_app_docker.sh

Step 5

Check for the following output files:

• A contour file in the output directory with the same filename as the input file, but with the .json extension.