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

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 estimated by a Gaussian mixture model is combined with a shape prior initially obtained by a statistical shape model (SSM). The region term can then take advantage of the high contrast between the background and foreground, while the shape prior guides the segmentation to the exterior of the cochlea and to less contrasted regions inside the cochlea. The prior is obtained via a non-rigid registration regularized by a statistical shape model. The SSM constrains the inner parts of the cochlea and ensures valid output shapes of the inner ear

3D Subject-Atlas Image Registration for Micro-Computed Tomography Based Characterization of Drug Delivery in the Murine Cochlea

A wide variety of hearing problems can potentially be treated with local drug delivery systems capable of delivering drugs directly to the cochlea over extended periods of time. Developing and testing such systems requires accurate quantification of drug concentration over time. A variety of techniques have been proposed for both direct and indirect measurement of drug pharmacokinetics; direct techniques are invasive, whereas many indirect techniques are imprecise because they rely on assumptions about the relationship between physiological response and drug concentrations. One indirect technique, however, is capable of quantifying drug pharmacokinetics very precisely: Micro-Computed tomography (micro-CT) can provide a non-invasive way to measure the concentration of a contrast agent in the cochlea over time. In this thesis, we propose a systematic approach for analyzing micro-CT images to measure concentrations of the contrast agent ioversol in mouse cochlea. This approach requires segmenting and classifying the intra-cochlea structures from micro-CT images, which is done via 3D atlas-subject registration to a published atlas of the mouse cochlea. Labels of each intra-cochlear structure in the atlas are propagated through the registration transformation to the corresponding structures in the micro-CT images. Pixel intensities are extracted from three key intra-cochlea structures: scala tympani (ST), scala vestibuli (SV), scala media (SM) in the micro-CT images, and these intensities are mapped into concentrations using a linear model between solution concentration and image intensity that is determined in a previous calibration step. To localize this analysis, the ST, SV, SM are divided into several discrete components, and the concentrations are estimated in each component using a weighted average with weights determined by solving a nonhomogeneous Poisson equation with Dirichlet boundary conditions on the component boundaries. We illustrate this entire system on a series of micro-CT images of an anesthetized mouse that include a baseline scan (with no contrast agent) and a series of scans after injection of ioversol into the cochlea

Bayesian Logistic Shape Model Inference: application to cochlear image segmentation

International audienceIncorporating 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

A Web-Based Automated Image Processing Research Platform for Cochlear Implantation-Related Studies

International audienceThe robust delineation of the cochlea and its inner structures combined with the detection of the electrode of a cochlear implant within these structures is essential for envisaging a safer, more individualized, routine image-guided cochlear implant therapy. We present Nautilus—a web-based research platform for automated pre- and post-implantation cochlear analysis. Nautilus delineates cochlear structures from pre-operative clinical CT images by combining deep learning and Bayesian inference approaches. It enables the extraction of electrode locations from a post-operative CT image using convolutional neural networks and geometrical inference. By fusing pre- and post-operative images, Nautilus is able to provide a set of personalized pre- and post-operative metrics that can serve the exploration of clinically relevant questions in cochlear implantation therapy. In addition, Nautilus embeds a self-assessment module providing a confidence rating on the outputs of its pipeline. We present a detailed accuracy and robustness analyses of the tool on a carefully designed dataset. The results of these analyses provide legitimate grounds for envisaging the implementation of image-guided cochlear implant practices into routine clinical workflows