Random walks with statistical shape prior for cochlea and inner ear segmentation in micro-CT images

Pujadas, Esmeralda Ruiz; Piella, Gemma; Kjer, Hans Martin; Ballester, Miguel Angel GonzÃ¡lez

doi:10.1007/s00138-017-0891-x

“…Although deep-learning-based methods were used in the AutoCasNet framework in this project, conventional 2D segmentation algorithms could replace them as the basic segmentation technique in this framework. However, conventional algorithms exhibiting good performance for cochlear segmentation have been observed to be computationally expensive 11 , 12 , 28 , 29 . In a future step, we propose to integrate learned prior shape models in the 2D segmentation algorithm through deep generative networks 50 , 55 .…”

Section: Discussionmentioning

confidence: 99%

“…Fully automatic algorithms based on active statistical shape modelling have been applied to cochlear segmentation 25 , 26 , but to attain an accurate statistical shape in different scenarios, a large amount of annotated data would be required 27 . Other proposed solutions such as atlas-based frameworks 11 , 12 and iterative random-walks algorithm with shape prior integration produced encouraging results for cochlea segmentation but are computationally expensive 28 , 29 . Moreover, with shape priors and atlas-based methods, segmentation might fail if the analysed image diverges from the average shape model, and this is often the case in malformations.…”

Section: Introductionmentioning

confidence: 99%

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

Hussain

¹

,

Lalande

²

,

Girum

³

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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“…The former approaches are mostly based on cochlear shape fitting based on template image registration (Baker and Barnes, 2005), parametric shape model (Baker, 2008). The supervised methods are based on statistical deformation models (Ruiz Pujadas et al, 2018) and deep learning (Lv et al, 2021;Raabid et al, 2021;Heutink et al, 2020).…”

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

Bayesian logistic shape model inference: Application to cochlear image segmentation

Wang

¹

,

Demarcy²,

Vandersteen

³

et al. 2022

Medical Image Analysis

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Incorporating shape information is essential for the delineation of many organs and anatomical structures in medical images. While previous work has mainly focused on parametric spatial transformations applied on reference template shapes, in this paper, we address the Bayesian inference of parametric shape models for segmenting medical images with the objective to provide interpretable results. The proposed framework defines a likelihood appearance probability and a prior label probability based on a generic shape function through a logistic function. A reference length parameter defined in the sigmoid controls the trade-off between shape and appearance information. The inference of shape parameters is performed within an Expectation-Maximisation approach where a Gauss-Newton optimization stage allows to provide an approximation of the posterior probability of shape parameters. This framework is applied to the segmentation of cochlea structures from clinical CT images constrained by a 10 parameter shape model. It is evaluated on three different datasets, one of which includes more than 200 patient images. The results show performances comparable to supervised methods and better than previously proposed unsupervised ones. It also enables an analysis of parameter distributions and the quantification of segmentation uncertainty including the effect of the shape model.

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“…The former approaches are mostly based on cochlear shape fitting based on template image registration (Baker and Barnes, 2005), parametric shape model (Baker, 2008). The supervised methods are based on statistical deformation models (Ruiz Pujadas et al, 2018) and deep learning (Lv et al, 2021;Raabid et al, 2021;Heutink et al, 2020).…”

Section: Comparison With the State-of-the-artmentioning

confidence: 99%

Bayesian Logistic Shape Model Inference: application to cochlea image segmentation

Zihao,

Thomas,

Clair

et al. 2021

Preprint

0

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Incorporating shape information is essential for the delineation of many organs and anatomical structures in medical images. While previous work has mainly focused on parametric spatial transformations applied on reference template shapes, in this paper, we address the Bayesian inference of parametric shape models for segmenting medical images with the objective to provide interpretable results. The proposed framework defines a likelihood appearance probability and a prior label probability based on a generic shape function through a logistic function. A reference length parameter defined in the sigmoid controls the trade-off between shape and appearance information. The inference of shape parameters is performed within an Expectation-Maximisation approach where a Gauss-Newton optimization stage allows to provide an approximation of the posterior probability of shape parameters. This framework is applied to the segmentation of cochlea structures from clinical CT images constrained by a 10 parameter shape model. It is evaluated on three different datasets, one of which includes more than 200 patient images. The results show performances comparable to supervised methods and better than previously proposed unsupervised ones. It also enables an analysis of parameter distributions and the quantification of segmentation uncertainty including the effect of the shape model.

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Random walks with statistical shape prior for cochlea and inner ear segmentation in micro-CT images

Cited by 6 publications

References 21 publications

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

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

Bayesian logistic shape model inference: Application to cochlear image segmentation

Bayesian Logistic Shape Model Inference: application to cochlea image segmentation

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