  In this work, we propose to explicitly use the landmarks of prostate to guide
the MR-TRUS image registration. We first train a deep neural network to
automatically localize a set of meaningful landmarks, and then directly
generate the affine registration matrix from the location of these landmarks.
For landmark localization, instead of directly training a network to predict
the landmark coordinates, we propose to regress a full-resolution distance map
of the landmark, which is demonstrated effective in avoiding statistical bias
to unsatisfactory performance and thus improving performance. We then use the
predicted landmarks to generate the affine transformation matrix, which
outperforms the clinicians' manual rigid registration by a significant margin
in terms of TRE.
