  Cochlear implants (CIs) are implantable medical devices that can restore the
hearing sense of people suffering from profound hearing loss. The CI uses a set
of electrode contacts placed inside the cochlea to stimulate the auditory nerve
with current pulses. The exact location of these electrodes may be an important
parameter to improve and predict the performance with these devices. Currently
the methods used in clinics to characterize the geometry of the cochlea as well
as to estimate the electrode positions are manual, error-prone and time
consuming. We propose a Markov random field (MRF) model for CI electrode
localization for cone beam computed tomography (CBCT) data-sets. Intensity and
shape of electrodes are included as prior knowledge as well as distance and
angles between contacts. MRF inference is based on slice sampling particle
belief propagation and guided by several heuristics. A stochastic search finds
the best maximum a posteriori estimation among sampled MRF realizations. We
evaluate our algorithm on synthetic and real CBCT data-sets and compare its
performance with two state of the art algorithms. An increase of localization
precision up to 31.5% (mean), or 48.6% (median) respectively, on real CBCT
data-sets is shown.
