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Inner ear scheme showing the cochlea and the semicircular canals which are the auditory and balance parts of the inner ear, respectively
Source publication
A cochlear implant is an electronic device which can restore sound to completely or partially deaf patients. For surgical planning, a patient-specific model of the inner ear must be built using high-resolution images accurately segmented. We propose a new framework for segmentation of micro-CT cochlear images using random walks, where a region term...
Citations
... As a starting point in related literature, traditional methods like atlas-based frameworks 11 for cochlear structure segmentation and inner ear fluid space 12 or iterative random-walk with shape priors for full inner ear segmentation in µ CT data 13 have been suggested in the past. Within the realm of deep learning based methods, there exist several closely related and recent studies that directly share the goal of automated analysis of inner ear structures from volumetric CT data. ...
Automated analysis of the inner ear anatomy in radiological data instead of time-consuming manual assessment is a worthwhile goal that could facilitate preoperative planning and clinical research. We propose a framework encompassing joint semantic segmentation of the inner ear and anatomical landmark detection of helicotrema, oval and round window. A fully automated pipeline with a single, dual-headed volumetric 3D U-Net was implemented, trained and evaluated using manually labeled in-house datasets from cadaveric specimen ( $$N=43$$ N = 43 ) and clinical practice ( $$N=9$$ N = 9 ). The model robustness was further evaluated on three independent open-source datasets ( $$N = 23{} + 7{} + 17$$ N = 23 + 7 + 17 scans) consisting of cadaveric specimen scans. For the in-house datasets, Dice scores of $$\text{0.97 and 0.94}$$ 0.97 and 0.94 , intersection-over-union scores of $$\text{0.94 and 0.89}$$ 0.94 and 0.89 and average Hausdorff distances of $$0.065{}$$ 0.065 and $$0.14{}$$ 0.14 voxel units were achieved. The landmark localization task was performed automatically with an average localization error of $$\text{3.3 and 5.2}$$ 3.3 and 5.2 voxel units. A robust, albeit reduced performance could be attained for the catalogue of three open-source datasets. Results of the ablation studies with 43 mono-parametric variations of the basal architecture and training protocol provided task-optimal parameters for both categories. Ablation studies against single-task variants of the basal architecture showed a clear performance benefit of coupling landmark localization with segmentation and a dataset-dependent performance impact on segmentation ability.
... 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). ...
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 to reference template shapes, in this paper, we address the Bayesian inference of parametric shape models for segmenting medical images with the objective of providing 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 in which a Gauss-Newton optimization stage provides an approximation of the posterior probability of the shape parameters. This framework is applied to the segmentation of cochlear 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.
... 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 [Heutink et al., 2020, Lv et al., 2021, Raabid et al., 2021. ...
This thesis aims to expose several applications of artificial intelligence (AI) for medical data processing and understanding. Medical imaging is a domain generating massive data, which thus requires more and more time for clinicians to process and analyze them. In this manuscript, we show how generative learning can help in many aspects of the processing, understanding, and modeling of CT images of the inner ear.First, we develop a deep generative model to solve a commonly encountered problem in CT imaging: the presence of metal artifacts. This model may allow clinicians to better assess the quality of cochlea implant (CI) positioning with a reduced presence of artifacts. To this end, a generative adversarial neural network (GAN) framework equipped with a specially designed loss function is proposed. That network was trained on a synthetic CT volume dataset resulting from the application of X-ray physics simulations.Second, since many deep learning segmentation methods fail to cope with explicit shape representations, we propose a Bayesian generative framework that addresses the issues of shape model inference in 3D images. We focus on the balance between shape and appearance through an Expectation-Maximisation (EM) approach. The method is applied to the segmentation of more than 200 patient CT volumes. The results show performances that are comparable to supervised methods and better than previously proposed unsupervised ones. Besides, we show how the proposed framework can estimate the uncertainty in the shape parameters.Third, we tackle the issue of the compact representation of CT images through a novel flow-based deep generative network. Generative models can create an implicit distribution of the imaging dataset from which one can generate samples. For a better representation, we proposed a Quasi-symplectic Langevin Variational Autoencoder (Langevin-VAE) that improves the current gradients, flow-based generative models. Finally, we propose an online framework for medical landmarks detection that can cope with the difficulty to manually position landmarks in volumetric images. The one shot training framework includes an offline step that only requires a single annotated image for training and is applied to the annotation of hundreds of images.
... 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). ...
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.
... Other proposed solutions such as atlas-based frameworks 11,12 and iterative random-walks algorithm www.nature.com/scientificreports/ 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. ...
... The relative high standard deviation for some metrics could be due to the fact that temporal bones contain air cells that resemble inner ear cavities on 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 . ...
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.
3D process plant models(PPMs) in the process industry normally consists of thousands of components. And, there are many similar local structures in the PPM. Due to the complex process flow, the topology relationship among components is very complicated. Therefore, designing a new PPM is quite time consuming. In order to shorten the design cycle, content based model retrieval for PPMs is an imperative requirement. In this paper, we propose a partial matching framework for PPMs based on graph matching aiming at improving design efficiency and realizing design reuse. The random walk algorithm is employed to distinguish similar local structures. Specifically, each PPM is represented by an undirected labeled graph. The local topological feature of each component is extracted based on the random walk algorithm. For partial matching, a subgraph isomorphism algorithm is introduced. The matching process is accelerated by using the local topological feature to generate an optimized initial state and alleviate the computation of feasible rules. Experimental results show the feasibility and effectiveness of our matching framework.
Purpose
Minimally invasive surgery is often built upon a time-consuming preoperative step consisting of segmentation and trajectory planning. At the temporal bone, a complete automation of these two tasks might lead to faster interventions and more reproducible results, benefiting clinical workflow and patient health.
Methods
We propose an automatic segmentation and trajectory planning pipeline for image-guided interventions at the temporal bone. For segmentation, we use a shape regularized deep learning approach that is capable of automatically detecting even the cluttered tiny structures specific for this anatomy. We then perform trajectory planning for both linear and nonlinear interventions on these automatically segmented risk structures.
Results
We evaluate the usability of segmentation algorithms for planning access canals to the cochlea and the internal auditory canal on 24 CT data sets of real patients. Our new approach achieves similar results to the existing semiautomatic method in terms of Dice but provides more accurate organ shapes for the subsequent trajectory planning step. The source code of the algorithms is publicly available.
Conclusion
Automatic segmentation and trajectory planning for various clinical procedures at the temporal bone are feasible. The proposed automatic pipeline leads to an efficient and unbiased workflow for preoperative planning.
