  Organ-at-risk contouring is still a bottleneck in radiotherapy, with many
deep learning methods falling short of promised results when evaluated on
clinical data. We investigate the accuracy and time-savings resulting from the
use of an interactive-machine-learning method for an organ-at-risk contouring
task. We compare the method to the Eclipse contouring software and find strong
agreement with manual delineations, with a dice score of 0.95. The annotations
created using corrective-annotation also take less time to create as more
images are annotated, resulting in substantial time savings compared to manual
methods, with hearts that take 2 minutes and 2 seconds to delineate on average,
after 923 images have been delineated, compared to 7 minutes and 1 seconds when
delineating manually. Our experiment demonstrates that
interactive-machine-learning with corrective-annotation provides a fast and
accessible way for non computer-scientists to train deep-learning models to
segment their own structures of interest as part of routine clinical workflows.
  Source code is available at
\href{https://github.com/Abe404/RootPainter3D}{this HTTPS URL}.
