A ChRIS plugin that runs an inference model to predict landmark points on leg images
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.. image:: https://img.shields.io/docker/v/fnndsc/pl-lld_inference?sort=semver :target: https://hub.docker.com/r/fnndsc/pl-lld_inference
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.. contents:: Table of Contents
This plugin application is part of the Leg-Length-Discrepancy project that measures leg lengths based off X-Ray images. This particular plugin operates on input mha format images, and infers the locations of several anatomical landmarks on the leg images. These locations are shown as heat maps (i.e. small fuzzy regions of probalities) that are superimposed on the original images. In addition, a prediction text file in csv format with the locations of the heatmaps is also generated.
lld_inference is a ChRIS ds-type application that works off inputs of leg images (X-Ray images converted to *mha format). For each image, the plugin attempts to determine where six landmarks are located -- three in each leg (in layman's terms):
For each inferred location of these three landmark in each leg, the plugin generates a heatmap image for that landmark. Thus, six separate images are created -- blank everywhere except for the heatmap. Taken all together, these six images are then superimposed onto the original image for comparison. In addition to output images, the plugin also generates a text file in csv format that describes the heatmap centroids in (x, y) coordinate pairs.
.. code::
docker run --rm fnndsc/pl-lld_inference lld_inference
[-f|--inputFileFilter <inputFileFilter>]
[-h|--help]
[--json] [--man] [--meta]
[--savejson <DIR>]
[-v|--verbosity <level>]
[--version]
<inputDir> <outputDir>
Arguments
.. code::
[-f|--inputFileFilter <inputFileFilter>]
A glob pattern string, default is "**/*.mha", representing the input
file pattern to analyze.
[-h] [--help]
If specified, show help message and exit.
[--json]
If specified, show json representation of app and exit.
[--man]
If specified, print (this) man page and exit.
[--meta]
If specified, print plugin meta data and exit.
[--modelType]
If specified, choose the specified model weights for inference.
Options are 1) lld 2) vv. Default is lld.
[--savejson <DIR>]
If specified, save json representation file to DIR and exit.
[-v <level>] [--verbosity <level>]
Verbosity level for app. Not used currently.
[--version]
If specified, print version number and exit.
Getting inline help is:
.. code:: bash
docker run --rm fnndsc/pl-lld_inference lld_inference --man
Run
~~~
.. ATTENTION::
``pl-lld_inference`` is built from a base image containing an Intel CPU
optimized image of Tensorflow. A (modern) Intel CPU is expected, AMD
probably won't work. The usage below has not been updated, the ``--gpus``
flag is not required.
You need to specify input and output directories using the `-v` flag to
`docker run`. Also set the output directory to be world writable
.. code:: bash
mkdir in out && chmod 777 out
docker run --rm -u $(id -u) \
-v $PWD/in:/incoming -v $PWD/out:/outgoing \
fnndsc/pl-lld_inference lld_inference \
/incoming /outgoing
Development
-----------
Build the Docker container:
.. code:: bash
docker build -t local/pl-lld_inference .
Run unit tests:
.. code:: bash
docker run --rm local/pl-lld_inference nosetests
For in-container debugging, mount the source directories appropriately:
.. code:: bash
cd pl-lld_inference
docker run -it --rm --gpus all \
-v $PWD/LLDcode:/opt/conda/lib/python3.6/site-packages/LLDcode \
-v $PWD/lld_inference:/opt/conda/lib/python3.6/site-packages/lld_inference \
-v $PWD/in:/incoming -v $PWD/out:/outgoing \
local/pl-lld_inference lld_inference \
/incoming /outgoing
Examples
--------
.. code:: bash
# Assume you have some *mha leg images... copy them to the input directory.
# Obviously adjust below as you see fit!
cd ~/some/dir
mkdir in out && chmod 777 out
cp *mha in
docker run --rm -u $(id -u) --gpus all \
-v $PWD/in:/incoming -v $PWD/out:/outgoing \
fnndsc/pl-lld_inference lld_inference \
/incoming /outgoing
_-30-_
.. image:: https://raw.githubusercontent.com/FNNDSC/cookiecutter-chrisapp/master/doc/assets/badge/light.png
:target: https://chrisstore.co
Content type
Image
Digest
sha256:991638a4f…
Size
2.8 GB
Last updated
over 1 year ago
docker pull fnndsc/pl-lld_inference