Unsupervised correspondence with combined geometric learning and imaging
  for radiotherapy applications
Edward G. A. Henderson Marcel van Herk Andrew F. Green Eliana M. Vasquez Osorio
The aim of this study was to develop a model to accurately identify
corresponding points between organ segmentations of different patients for
radiotherapy applications. A model for simultaneous correspondence and
interpolation estimation in 3D shapes was trained with head and neck organ
segmentations from planning CT scans. We then extended the original model to
incorporate imaging information using two approaches: 1) extracting features
directly from image patches, and 2) including the mean square error between
patches as part of the loss function. The correspondence and interpolation
performance were evaluated using the geodesic error, chamfer distance and
conformal distortion metrics, as well as distances between anatomical
landmarks. Each of the models produced significantly better correspondences
than the baseline non-rigid registration approach. The original model performed
similarly to the model with direct inclusion of image features. The best
performing model configuration incorporated imaging information as part of the
loss function which produced more anatomically plausible correspondences. We
will use the best performing model to identify corresponding anatomical points
on organs to improve spatial normalisation, an important step in outcome
modelling, or as an initialisation for anatomically informed registrations. All
our code is publicly available at
https://github.com/rrr-uom-projects/Unsup-RT-Corr-Net