Evidence map›Paper›PMID 30468968›Full record

ArticleMedical image analysis2019

Automatic graph-based method for localization of cochlear implant electrode arrays in clinical CT with sub-voxel accuracy.

Yiyuan Zhao, Srijata Chakravorti, Robert F Labadie, Benoit M Dawant, Jack H Noble

Abstract read
In one paragraph

Article in Medical image analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed.

  1. Article
  2. Simulation of apically grounded cochlear implant stimuli using neural stimulation models.International journal of computer assisted radiology and surgery · 2026
    Article
  3. Article
  4. A Unified Deep-Learning-Based Framework for Cochlear Implant Electrode Array Localization.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023
    Article
  5. Article
  6. Article
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  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Auditory performance of post-lingually deafened adult cochlear implant recipients using electrode deactivation based on postoperative cone beam CT images.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2021
    Article
  15. A Graph-Based Method for Optimal Active Electrode Selection in Cochlear Implants.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020
    Article
  16. Article
  17. Article
  18. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yiyuan ZhaoDepartment of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA. Electronic address: yiyuan.zhao@vanderbilt.edu.
Srijata ChakravortiDepartment of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA.
Robert F LabadieDepartment of Otolaryngology - Head and Neck Surgery, Vanderbilt University Medical Center, Nashville, TN 37235, USA.
Benoit M DawantDepartment of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA.
Jack H NobleDepartment of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA. Electronic address: jack.noble@vanderbilt.edu.

Funding

Research Supplements to Promote Diversity in Health-Related ResearchR01DC008408 · NIDCD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI LABADIE, ROBERT F · 2007 to 2022
$7.6M
Model-based Cochlear Implant ProgrammingR01DC014037 · NIDCD · VANDERBILT UNIVERSITY · PI NOBLE, JACK · 2014 to 2024
$5.0M
Computer-assisted, image-guided programming of Cochlear ImplantsR01DC014462 · NIDCD · VANDERBILT UNIVERSITY · PI DAWANT, BENOIT M. · 2016 to 2020
$2.9M
NIDCD NIH HHS R01 DC008408NIDCD NIH HHS R01 DC014037NIDCD NIH HHS R01 DC014462
6 · The paper itself

Abstract

Cochlear implants (CIs) are neural prosthetics that provide a sense of sound to people who experience severe to profound hearing loss. Recent studies have demonstrated a correlation between hearing outcomes and intra-cochlear locations of CI electrodes. Our group has been conducting investigations on this correlation and has been developing an image-guided cochlear implant programming (IGCIP) system to program CI devices to improve hearing outcomes. One crucial step that has not been automated in IGCIP is the localization of CI electrodes in clinical CTs. Existing methods for CI electrode localization do not generalize well on large-scale datasets of clinical CTs implanted with different brands of CI arrays. In this paper, we propose a novel method for localizing different brands of CI electrodes in clinical CTs. We firstly generate the candidate electrode positions at sub-voxel resolution in a whole head CT by thresholding an up-sampled feature image and voxel-thinning the result. Then, we use a graph-based path-finding algorithm to find a fixed-length path that consists of a subset of the candidates as the localization result. Validation on a large-scale dataset of clinical CTs shows that our proposed method outperforms the state-of-art CI electrode localization methods and achieves a mean error of 0.12 mm when compared to expert manual localization results. This represents a crucial step in translating IGCIP from the laboratory to large-scale clinical use.

Indexed as

Cochlear ImplantsElectrodes, ImplantedAlgorithmsCochleaHumansRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray ComputedCI electrode arrayCochlear implantGraph searchSegmentation

Identifiers

PMID30468968
PMCPMC6543817

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.