Automatic Cochlear Length and Volume Size Estimation

Al-Dhamari, Ibraheem; Bauer, Sabine; Paulus, Dietrich; Helal, R. A. M. A.; Lisseck, Friedrich; Jacob, R.

doi:10.1007/978-3-030-01201-4_7

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…This can reduce variability of basal turn measurements by allowing a standard orientation of the plane used for linear measurement 42,43 . Previous histological approaches may have underestimated CDL since the plane used for measurement could not be predictably placed perpendicular to the central cochlear axis 19,21,36 . Similarly, such studies were limited by the necessity of specialized manual measurements, which could be subject to inter‐observer variability 30 …”

Section: Discussionmentioning

confidence: 99%

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Fully Automated Measurement of Cochlear Duct Length From Clinical Temporal Bone Computed Tomography

et al. 2021

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Objectives/Hypothesis: To present and validate a novel fully automated method to measure cochlear dimensions, including cochlear duct length (CDL).Study Design: Cross-sectional study.Methods: The computational method combined 1) a deep learning (DL) algorithm to segment the cochlea and otic capsule and 2) geometric analysis to measure anti-modiolar distances from the round window to the apex. The algorithm was trained using 165 manually segmented clinical computed tomography (CT). A Testing group of 159 CTs were then measured for cochlear diameter and width (A-and B-values) and CDL using the automated system and compared against manual measurements. The results were also compared with existing approaches and historical data. In addition, pre-and postimplantation scans from 27 cochlear implant recipients were studied to compare predicted versus actual array insertion depth.Results: Measurements were successfully obtained in 98.1% of scans. The mean CDL to 900 was 35.52 mm (SD, 2.06; range, [30.91-40.50]), the mean A-value was 8.88 mm (0.47; [7.67-10.49]), and mean B-value was 6.38 mm (0.42; [5.16-7.38]). The R 2 fit of the automated to manual measurements was 0.87 for A-value, 0.70 for B-value, and 0.71 for CDL. For anti-modiolar arrays, the distance between the imaged and predicted array tip location was 0.57 mm (1.25; [0.13-5.28]). Conclusion:Our method provides a fully automated means of cochlear analysis from clinical CTs. The distribution of CDL, dimensions, and cochlear quadrant lengths is similar to those from historical data. This approach requires no radiographic experience and is free from user-related variation.

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Section: Discussionmentioning

confidence: 99%

“…Most current methods to measure CDL from clinical CTs require manually marking points on planar representations of the CT 15–21 . These points are used to derive linear dimensions, which in turn can be used to extrapolate the CDL through the use of mathematical formulas 16–18 .…”

Section: Introductionmentioning

confidence: 99%

Fully Automated Measurement of Cochlear Duct Length From Clinical Temporal Bone Computed Tomography

et al. 2021

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Abstract: Automatic Estimation of Cochlear Duct Length and Volume Size

et al. 2019

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Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network

Hussain

Lalande

Girum

et al. 2021

Sci Rep

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Temporal bone CT-scan is a prerequisite in most surgical procedures concerning the ear such as cochlear implants. The 3D vision of inner ear structures is crucial for diagnostic and surgical preplanning purposes. Since clinical CT-scans are acquired at relatively low resolutions, improved performance can be achieved by registering patient-specific CT images to a high-resolution inner ear model built from accurate 3D segmentations based on micro-CT of human temporal bone specimens. This paper presents a framework based on convolutional neural network for human inner ear segmentation from micro-CT images which can be used to build such a model from an extensive database. The proposed approach employs an auto-context based cascaded 2D U-net architecture with 3D connected component refinement to segment the cochlear scalae, semicircular canals, and the vestibule. The system was formulated on a data set composed of 17 micro-CT from public Hear-EU dataset. A Dice coefficient of 0.90 and Hausdorff distance of 0.74 mm were obtained. The system yielded precise and fast automatic inner-ear segmentations.

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Automatic Cochlear Length and Volume Size Estimation

Cited by 4 publications

References 17 publications

Fully Automated Measurement of Cochlear Duct Length From Clinical Temporal Bone Computed Tomography

Fully Automated Measurement of Cochlear Duct Length From Clinical Temporal Bone Computed Tomography

Abstract: Automatic Estimation of Cochlear Duct Length and Volume Size

Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network

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