Metadata-Version: 2.1
Name: spleenseg
Version: 1.2.0
Summary: A ChRIS DS plugin heavily hacked off project MONAI's spleen segmenation notebook
Home-page: https://github.com/FNNDSC/pl-monai_spleenseg
Author: FNNDSC
Author-email: dev@babyMRI.org
License: MIT
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Provides-Extra: dev
Provides-Extra: none
License-File: LICENSE

Spleen 3D image segmentation (MONAI)
====================================

|Version| |MIT License| |ci|

``pl-monai_spleenseg`` is a `ChRIS <https://chrisproject.org/>`__ *DS*
plugin based off Project MONAI’s spleen segmentation exemplar. This
plugin implements the training and inference phases as two distinct
modes of operation. For training, input files are a set of training
examples (images and segmented images) and output files are training
plots and weight (model) files in ``pth`` and ``ONNX`` format. For
inference, input files are a model file and an image to segment.

Abstract
--------

Based off Project MONAI’s `spleen segmentation
notebook <https://github.com/Project-MONAI/tutorials/blob/main/3d_segmentation/spleen_segmentation_3d.ipynb>`__,
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 <https://chrisproject.org/>`__\ *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 <https://apptainer.org/>`__ (a.k.a. Singularity) to run
``pl-monai_spleenseg`` as a container:

.. code:: shell

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

To print its available options, run:

.. code:: shell

   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 <http://medicaldecathelon.com>`__. To get this data,
first set an environment variable pointing at the directory to contain
the pulled and unpacked data:

.. code:: bash

   export MONAI_DATA_DIR=/some/dir

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

.. code:: python

   # 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):

.. code:: bash

   python trainingDataPull.py

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

.. code:: shell

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

Development
-----------

Instructions for developers.

Building
~~~~~~~~

Build a local container image:

.. code:: shell

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

Running
~~~~~~~

Mount the source code ``spleenseg_train.py`` into a container to try out
changes without rebuild.

.. code:: shell

   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

Testing
~~~~~~~

Run unit tests using ``pytest``. It’s recommended to rebuild the image
to ensure that sources are up-to-date. Use the option
``--build-arg extras_require=dev`` to install extra dependencies for
testing.

.. code:: shell

   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 <.github/workflows/ci.yml>`__. This section is about how to do
those steps manually.

Increase Version Number
~~~~~~~~~~~~~~~~~~~~~~~

Increase the version number in ``setup.py`` and commit this file.

Push Container Image
~~~~~~~~~~~~~~~~~~~~

Build and push an image tagged by the version. For example, for version
``1.2.3``:

::

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

Get JSON Representation
~~~~~~~~~~~~~~~~~~~~~~~

Run
```chris_plugin_info`` <https://github.com/FNNDSC/chris_plugin#usage>`__
to produce a JSON description of this plugin, which can be uploaded to
*ChRIS*.

.. code:: shell

   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

Intructions on how to upload the plugin to *ChRIS* can be found here:
https://chrisproject.org/docs/tutorials/upload_plugin

*-30-*

.. |Version| image:: https://img.shields.io/docker/v/fnndsc/pl-monai_spleenseg?sort=semver
   :target: https://hub.docker.com/r/fnndsc/pl-monai_spleenseg
.. |MIT License| image:: https://img.shields.io/github/license/fnndsc/pl-monai_spleenseg
   :target: https://github.com/FNNDSC/pl-monai_spleenseg/blob/main/LICENSE
.. |ci| image:: https://github.com/FNNDSC/pl-monai_spleenseg/actions/workflows/ci.yml/badge.svg
   :target: https://github.com/FNNDSC/pl-monai_spleenseg/actions/workflows/ci.yml


