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Cochlear implants (CIs) are implantable medical devices that can restore thehearing sense of people suffering from profound hearing loss. The CI uses a setof electrode contacts placed inside the cochlea to stimulate the auditory nervewith current pulses. The exact location of these electrodes may be an importantparameter to improve and predict the performance with these devices. Currentlythe methods used in clinics to characterize the geometry of the cochlea as wellas to estimate the electrode positions are manual, error-prone and timeconsuming. We propose a Markov random field (MRF) model for CI electrodelocalization for cone beam computed tomography (CBCT) data-sets. Intensity andshape of electrodes are included as prior knowledge as well as distance andangles between contacts. MRF inference is based on slice sampling particlebelief propagation and guided by several heuristics. A stochastic search findsthe best maximum a posteriori estimation among sampled MRF realizations. Weevaluate our algorithm on synthetic and real CBCT data-sets and compare itsperformance with two state of the art algorithms. An increase of localizationprecision up to 31.5% (mean), or 48.6% (median) respectively, on real CBCTdata-sets is shown.

Localization of Cochlear Implant Electrodes from Cone Beam Computed Tomography using Particle Belief Propagation