spleenseg 1.2.2

Abstract

Based off Project MONAI’s spleen segmentation notebook, this plugin implements both the training and inference phases of the notebook, using data supplied in the parent plugin (see Implementation). For the most part, the python notebook code can be used verbatim in the plugin; however, in this example some deeper refactoring (adding typing, and some refactoring) to improve its use as a stand-alone application.

In general, notebooks are not ideal for batch usage, and often cells repeat code used elsewhere in the notebook. This plugin code consolidated and generalized many of these cells into functions, reducing the overall code footprint considerably.

For the training phase, the parent plugin provides input images (training and labeled) and the output is a model (pth and ONNX format). For the inference phase, the input is a model file, and an image with the output being a segmented result.

Implementation

The original notebook is a largely self-contained monolithic application. Exemplar input data is pulled from the web, and the notebook proceeds from there. In the case of this ChRIS plugin, some straightforward organizational changes are necessary. The training data is assumed to already have been downloaded a priori and is provided to this plugin by its parent. Outputs of the training are model weight filesTh.

Installation

pl-monai_spleenseg is a ChRIS plugin, meaning it can run from either within ChRIS or the command-line.

Local Usage

To get started with local command-line usage, use Apptainer (a.k.a. Singularity) to run pl-monai_spleenseg as a container:

apptainer exec docker://fnndsc/pl-monai_spleenseg spleenseg_train \
            [--args values...] input/ output/

To print its available options, run:

apptainer exec docker://fnndsc/pl-monai_spleenseg spleenseg_train --help

Examples

spleenseg_train requires two positional arguments: a directory containing input data, and a directory containing output data (graphs and “model” files). In this plugin, data is downloaded from medicaldecathelon. To get this data, first set an environment variable pointing at the directory to contain the pulled and unpacked data:

export MONAI_DATA_DIR=/some/dir

now, you can pull the data with this python snippet:

# You probably will need to
#   pip install -q "monai-weekly[gdown, nibabel, tqdm, ignite]"
from monai.apps import download_and_extract

directory = os.environ.get("MONAI_DATA_DIRECTORY")
root_dir = tempfile.mkdtemp() if directory is None else directory
print(root_dir)


resource = "https://msd-for-monai.s3-us-west-2.amazonaws.com/Task09_Spleen.tar"
md5 = "410d4a301da4e5b2f6f86ec3ddba524e"
compressed_file = os.path.join(root_dir, "Task09_Spleen.tar")
data_dir = os.path.join(root_dir, "Task09_Spleen")
if not os.path.exists(data_dir):
    download_and_extract(resource, compressed_file, root_dir, md5)

Or simply run the supplied trainingDataPull.py script (which is essentially the above code):

python trainingDataPull.py

Create some output directory, and using our $MONAI_DATA_DIR, we can run the plugin:

mkdir outgoing/
apptainer exec docker://fnndsc/pl-monai_spleenseg:latest spleenseg_train \
        [--args] $MONAI_DATA_DIR outgoing/

Development

Instructions for developers.

docker build -t localhost/fnndsc/pl-monai_spleenseg .

docker run --rm -it --userns=host -u $(id -u):$(id -g) \
    -v $PWD/spleenseg_train.py:/usr/local/lib/python3.11/site-packages/spleenseg_train.py:ro \
    -v $PWD/in:/incoming:ro -v $PWD/out:/outgoing:rw -w /outgoing \
    localhost/fnndsc/pl-monai_spleenseg spleenseg_train /incoming /outgoing

docker build -t localhost/fnndsc/pl-monai_spleenseg:dev --build-arg extras_require=dev .
docker run --rm -it localhost/fnndsc/pl-monai_spleenseg:dev pytest

Release

Steps for release can be automated by Github Actions. This section is about how to do those steps manually.

docker build -t docker.io/fnndsc/pl-monai_spleenseg:1.2.3 .
docker push docker.io/fnndsc/pl-monai_spleenseg:1.2.3

docker run --rm docker.io/fnndsc/pl-monai_spleenseg:1.2.3 chris_plugin_info \
            -d docker.io/fnndsc/pl-monai_spleenseg:1.2.3 > chris_plugin_info.json