  Anatomical landmark correspondences in medical images can provide additional
guidance information for the alignment of two images, which, in turn, is
crucial for many medical applications. However, manual landmark annotation is
labor-intensive. Therefore, we propose an end-to-end deep learning approach to
automatically detect landmark correspondences in pairs of two-dimensional (2D)
images. Our approach consists of a Siamese neural network, which is trained to
identify salient locations in images as landmarks and predict matching
probabilities for landmark pairs from two different images. We trained our
approach on 2D transverse slices from 168 lower abdominal Computed Tomography
(CT) scans. We tested the approach on 22,206 pairs of 2D slices with varying
levels of intensity, affine, and elastic transformations. The proposed approach
finds an average of 639, 466, and 370 landmark matches per image pair for
intensity, affine, and elastic transformations, respectively, with spatial
matching errors of at most 1 mm. Further, more than 99% of the landmark pairs
are within a spatial matching error of 2 mm, 4 mm, and 8 mm for image pairs
with intensity, affine, and elastic transformations, respectively. To
investigate the utility of our developed approach in a clinical setting, we
also tested our approach on pairs of transverse slices selected from follow-up
CT scans of three patients. Visual inspection of the results revealed landmark
matches in both bony anatomical regions as well as in soft tissues lacking
prominent intensity gradients.
