TY - GEN

T1 - Localization of cochlear implant electrodes from cone beam computed tomography using particle belief propagation

AU - Hachmann, Tom Hendrik

AU - Krüger, Benjamin

AU - Rosenhahn, Bodo

AU - Nogueira, Waldo

N1 - Funding Information: ∗H.H. and B.R. were partly funded by the Lower Saxony Ministry of Science and Culture under grant number ZN 3491 within the Lower Saxony “Vorab“ of the Volkswagen Foundation and by the Federal Ministry of Education and Research (BMBF), Germany under the project LeibnizKILabor (grant no. 01DD20003) and the Center for Digital Innovations (ZDIN). W.N. and B.K. received funding for this research from the Deutsche Forschungs-gemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy EXC 2177/1 ’Hearing4all’, DFG project numbers 390895286 and 396932747.

PY - 2021/4/13

Y1 - 2021/4/13

N2 - 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.

AB - 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.

KW - Automatic localization

KW - Cochlear implant

KW - Electrode

KW - Markov random fields

UR - http://www.scopus.com/inward/record.url?scp=85107184090&partnerID=8YFLogxK

U2 - 10.48550/arXiv.2103.10434

DO - 10.48550/arXiv.2103.10434

M3 - Conference contribution

AN - SCOPUS:85107184090

SN - 978-1-6654-2947-4

T3 - Proceedings - International Symposium on Biomedical Imaging

SP - 593

EP - 597

BT - 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI 2021)

PB - IEEE Computer Society

T2 - 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021

Y2 - 13 April 2021 through 16 April 2021

ER -