A segmentation method to obtain a complete geometry model of the hearing organ

Poznyakovskiy, Anton A.; Zahnert, Thomas; Kalaidzidis, Yannis; Lazurashvili, N.; Schmidt, Rolf; Hardtke, H.‐J.; Fischer, Björn; Yarin, Yury M.

doi:10.1016/j.heares.2011.06.009

Cited by 22 publications

(15 citation statements)

References 45 publications

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“…The challenge of cochlear centerline extraction has already been dealt with (Baker and Barnes, 2004;Verbist et al, 2009;Poznyakovskiy et al, 2011;Gunz et al, 2012). A flowchart of the method applied in this study and the results are respectively given on Fig.…”

Section: Automated Centerline Extractionmentioning

confidence: 99%

“…In order to evaluate the internal dimensions of the cochlea, cross-section measurements were performed in radial planes (a.k.a mid-modiolar cross-sections) (Zrunek et al, 1980;Zrunek and Lischka, 1981;Erixon et al, 2009), in parallel planes such as histological sections (Biedron et al, 2010) and in planes normal to the centerline (Poznyakovskiy et al, 2011;Avci et al, 2014). This last method does not induce measurement errors (e.g.…”

Section: Cochlear Moving Framementioning

confidence: 99%

“…As important cochlear anatomical structures such as the basilar membrane are not visible in CT imaging, analyses of the cochlea are commonly performed using µCT images (Verbist et al, 2009;Poznyakovskiy et al, 2011;Gunz et al, 2012;Shin et al, 2013;Avci et al, 2014;Ceresa et al, 2014). In order to improve contrast, temporal bone samples can be fixed and stained before imaging (Poznyakovskiy et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…In order to improve contrast, temporal bone samples can be fixed and stained before imaging (Poznyakovskiy et al, 2011). Cochlear fluids are commonly removed (Postnov et al, 2006;Poznyakovskiy et al, 2011;Avci et al, 2014;Ceresa et al, 2014) but this operation requires to drill through the round and oval windows and to replace the perilymph with air by suction. The influence of this method on the geometry of the membranous structures is unknown (Rau et al, 2013) and µCT scanning with fluid removal cannot be considered as nondestructive.…”

Section: Introductionmentioning

confidence: 99%

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Automated analysis of human cochlea shape variability from segmented μ CT images

Demarcy

Vandersteen

Guevara³

et al. 2017

Computerized Medical Imaging and Graphics

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The aim of this study is to define an automated and reproducible framework for cochlear anatomical analysis from high-resolution segmented images and to provide a comprehensive and objective shape variability study suitable for cochlear implant design and surgery planning. For the scala tympani (ST), the scala vestibuli (SV) and the whole cochlea, the variability of the arc lengths and the radial and longitudinal components of the lateral, central and modiolar paths are studied. The robustness of the automated cochlear coordinate system estimation is validated with synthetic and real data. Cochlear cross-sections are statistically analyzed using area, height and width measurements. The cross-section tilt angle is objectively measured and this data documents a significant feature for occurrence of surgical trauma.

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Section: Automated Centerline Extractionmentioning

confidence: 99%

Section: Cochlear Moving Framementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automated analysis of human cochlea shape variability from segmented μ CT images

Demarcy

Vandersteen

Guevara³

et al. 2017

Computerized Medical Imaging and Graphics

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“…One work is based on 2D snakes to obtain the cochlea, where a high level of user interaction to locate the initial contour and adjustment of the parameters are required. 5 Another approach is based on active shape models 6 where the segmentation is performed by first building a high resolution statistical model from several training micro-CT images, which can then be used to predict the position of anatomical structures in CT images.…”

Section: Introductionmentioning

confidence: 99%

Cochlea segmentation using iterated random walks with shape prior

Pujadas

Kjer

Vera³

et al. 2016

SPIE Proceedings

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Cochlear implants can restore hearing to deaf or partially deaf patients. In order to plan the intervention, a model from high resolution µCT images is to be built from accurate cochlea segmentations and then, adapted to a patient-specific model. Thus, a precise segmentation is required to build such a model. We propose a new framework for segmentation of µCT cochlear images using random walks where a region term is combined with a distance shape prior weighted by a confidence map to adjust its influence according to the strength of the image contour. Then, the region term can take advantage of the high contrast between the background and foreground and the distance prior guides the segmentation to the exterior of the cochlea as well as to less contrasted regions inside the cochlea. Finally, a refinement is performed preserving the topology using a topological method and an error control map to prevent boundary leakage. We tested the proposed approach with 10 datasets and compared it with the latest techniques with random walks and priors. The experiments suggest that this method gives promising results for cochlea segmentation.

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