responsive video
@@ -10,20 +10,22 @@ Automatic Contouring Project
Project aim
-----------
-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 time
-delays in both contour generation and correction. This study designed and
-evaluated a 2D U-Net architecture with two primary aims:
+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 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.
+ 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.
+The intent for this research is to act as a template for future work extending
+to other organs.
Video Demonstrating Usage
@@ -33,9 +35,13 @@ Best viewed at 720p+ resolution.
.. raw:: html
- <embed>
- <iframe width="560" height="315" src="https://www.youtube.com/embed/fMCv5i6GJWI" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
- </embed>
+ <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>
@@ -48,14 +54,16 @@ evaluates 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.
-Consider joining the PyMedPhys `mailing list <https://groups.google.com/g/pymedphys?pli=1>`_ to
-be notified of release.
+Consider joining the PyMedPhys `mailing list
+<https://groups.google.com/g/pymedphys?pli=1>`_ to be notified of release.
Basic Implementation
--------------------
-Details on how to implement the project into your own workflow will be provided once the code migrates from the experimental division of PyMedPhys into the main code base. Watch this space.
+Details on how to implement the project into your own workflow will be provided
+once the code migrates from the experimental division of PyMedPhys into the main
+code base. Watch this space.
References
