  Purpose: Deformable Image Registration (DIR) can benefit from additional
guidance using corresponding landmarks in the images. However, the benefits
thereof are largely understudied, especially due to the lack of automatic
landmark detection methods for three-dimensional (3D) medical images.
  Approach: We present a Deep Convolutional Neural Network (DCNN), called
DCNN-Match, that learns to predict landmark correspondences in 3D images in a
self-supervised manner. We trained DCNN-Match on pairs of Computed Tomography
(CT) scans containing simulated deformations. We explored five variants of
DCNN-Match that use different loss functions and assessed their effect on the
spatial density of predicted landmarks and the associated matching errors. We
also tested DCNN-Match variants in combination with the open-source
registration software Elastix to assess the impact of predicted landmarks in
providing additional guidance to DIR.
  Results: We tested our approach on lower-abdominal CT scans from cervical
cancer patients: 121 pairs containing simulated deformations and 11 pairs
demonstrating clinical deformations. The results showed significant improvement
in DIR performance when landmark correspondences predicted by DCNN-Match were
used in the case of simulated (p = $0e^0$) as well as clinical deformations (p
= 0.030). We also observed that the spatial density of the automatic landmarks
with respect to the underlying deformation affect the extent of improvement in
DIR. Finally, DCNN-Match was found to generalize to Magnetic Resonance Imaging
(MRI) scans without requiring retraining, indicating easy applicability to
other datasets.
  Conclusions: DCNN-Match learns to predict landmark correspondences in 3D
medical images in a self-supervised manner, which can improve DIR performance.
