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Automatic Contouring Project
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Project aim
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Accurate contouring is a critical aspect of safe and effective treatment
delivery in radiotherapy (RT). Current limitations in clinical practice include
large intra and inter-observer variances (IOV), as well as time delays in both
contour generation and correction. This study designed and evaluated a 2D U-Net
architecture with two primary aims:

1) Automate a time consuming aspect of canine radiotherapy. Specifically, vacuum
   bag segmentation has been reported to take approximately 30 minutes to
   contour manually.

2) Evaluate the ability of a 2D U-Net model to achieve expert level performance
   as defined by Nikolov et al.'s surface dice similarity coefficient [1]_ with
   respect to IOV.

The intent for this research is to act as a template for future work extending
to other organs.


Video Demonstrating Usage
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Best viewed at 720p+ resolution.

.. raw:: html

    <embed><style>.embed-container { position: relative; padding-bottom:
    56.25%; height: 0; overflow: hidden; max-width: 100%; } .embed-container
    iframe, .embed-container object, .embed-container embed { position:
    absolute; top: 0; left: 0; width: 100%; height: 100%; }</style><div
    class='embed-container'><iframe
    src='https://www.youtube.com/embed/fMCv5i6GJWI' frameborder='0'
    allowfullscreen></iframe></div></embed>


Details
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The `Masters thesis
<https://github.com/matthewdeancooper/masters_thesis/blob/master/main.pdf>`_
developed during this software project has been publicly released to provide a
detailed description of the work. It provides details of the model architecture,
and examines performance under multiple loss functions. In addition, this work
discusses the development of a second model designed to fulfil the need for
contouring to become part of regular quality assurance testing.


References
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.. [1] Nikolov et al. "Deep learning to achieve clinically applicable segmentation of head
    and neck anatomy for radiotherapy." (2018): https://arxiv.org/pdf/1809.04430.pdf