Evidence map›Paper›PMID 39224385›Full record

ArticleHeliyon2024

Automated segmentation of clinical CT scans of the cochlea and analysis of the cochlea's vertical profile.

Michael Siebrecht, Jeroen J Briaire, Berit M Verbist, Randy K Kalkman, Johan H M Frijns

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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

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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

1 citing paper in PubMed.

  1. Review
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.

Michael SiebrechtDepartment of Otorhinolaryngology and Head & Neck Surgery, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, the Netherlands.
Jeroen J BriaireDepartment of Otorhinolaryngology and Head & Neck Surgery, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, the Netherlands.
Berit M VerbistDepartment of Radiology, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, the Netherlands.
Randy K KalkmanDepartment of Otorhinolaryngology and Head & Neck Surgery, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, the Netherlands.
Johan H M FrijnsDepartment of Otorhinolaryngology and Head & Neck Surgery, Leiden University Medical Center, PO Box 9600, 2300 RC, Leiden, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Knowledge of the cochlear anatomy in individual patients is helpful for improving electrode selection and placement during cochlear implantation, as well as in surgical planning. The aim of this study was to develop a model-free automated segmentation algorithm to obtain 3D surfaces from clinical computed tomography (CT) scans that describe an individual's cochlear anatomy and can be used to quantitatively analyze the cochlea's vertical trajectory. Methods: Clinical CT scans were re-oriented and re-sliced to obtain mid-modiolar slices. Using these slices, we segmented the cross-section of the cochlea. Results: 3D surfaces were obtained for the first 1.5 turns of 648 cochleae. Validation of our algorithm against the manually segmented ground truth obtained from 8 micro-CT scans showed good agreement, with 90 % area overlap and an average distance of 0.11 mm between the segmentation contours. The average cochlear duct length for the basal turn was 16.1 mm along the central path and 22.4 mm along the outer wall. The use of an intrinsic, observer-independent coordinate system and principal component analysis enabled unambiguous quantitative evaluation of the vertical trajectory of the cochlea, revealing only a weak correlation between the symmetry of the commonly used basal turn diameters (B-ratio of A and B diameters) and the profile of the vertical trajectory. Conclusion: A model-free segmentation algorithm can achieve similar accuracy as previously published methods relying on statistical shapes. Quantitative analysis of the vertical trajectory can replace the categorization into rollercoaster, sloping, or intermediate vertical trajectory types.

Indexed as

Automated image analysisCochlear anatomyCochlear implantCTSegmentationSensorineural hearing loss

Identifiers

PMID39224385
PMCPMC11367034

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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.