  We present a meta-learning framework for interactive medical image
registration. Our proposed framework comprises three components: a
learning-based medical image registration algorithm, a form of user interaction
that refines registration at inference, and a meta-learning protocol that
learns a rapidly adaptable network initialization. This paper describes a
specific algorithm that implements the registration, interaction and
meta-learning protocol for our exemplar clinical application: registration of
magnetic resonance (MR) imaging to interactively acquired, sparsely-sampled
transrectal ultrasound (TRUS) images. Our approach obtains comparable
registration error (4.26 mm) to the best-performing non-interactive
learning-based 3D-to-3D method (3.97 mm) while requiring only a fraction of the
data, and occurring in real-time during acquisition. Applying sparsely sampled
data to non-interactive methods yields higher registration errors (6.26 mm),
demonstrating the effectiveness of interactive MR-TRUS registration, which may
be applied intraoperatively given the real-time nature of the adaptation
process.
