Self-supervised Landmark Learning with Deformation Reconstruction and
  Cross-subject Consistency Objectives
Chun-Hung Chao Marc Niethammer
A Point Distribution Model (PDM) is the basis of a Statistical Shape Model
(SSM) that relies on a set of landmark points to represent a shape and
characterize the shape variation. In this work, we present a self-supervised
approach to extract landmark points from a given registration model for the
PDMs. Based on the assumption that the landmarks are the points that have the
most influence on registration, existing works learn a point-based registration
model with a small number of points to estimate the landmark points that
influence the deformation the most. However, such approaches assume that the
deformation can be captured by point-based registration and quality landmarks
can be learned solely with the deformation capturing objective. We argue that
data with complicated deformations can not easily be modeled with point-based
registration when only a limited number of points is used to extract
influential landmark points. Further, landmark consistency is not assured in
existing approaches In contrast, we propose to extract landmarks based on a
given registration model, which is tailored for the target data, so we can
obtain more accurate correspondences. Secondly, to establish the anatomical
consistency of the predicted landmarks, we introduce a landmark discovery loss
to explicitly encourage the model to predict the landmarks that are
anatomically consistent across subjects. We conduct experiments on an
osteoarthritis progression prediction task and show our method outperforms
existing image-based and point-based approaches.