A Deep Learning based Fast Signed Distance Map Generation

Signed distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning. The computational complexity of SDM for 3D parametric shapes is often a bottleneck in many applications, thus limiting their interest. In this paper, we propose a learning based SDM generation neural network which is demonstrated on a tridimensional cochlea shape model parameterized by 4 shape parameters. The proposed SDM Neural Network generates a cochlea signed distance map depending on four input parameters and we show that the deep learning approach leads to a 60 fold improvement in the time of computation compared to more classical SDM generation methods. Therefore, the proposed approach achieves a good trade-off between accuracy and efficiency

A Deep Learning based Fast Signed Distance Map Generation

International audienceSigned distance map (SDM) is a common representation of surfaces in medical image analysis and machine learning. The computational complexity of SDM for 3D parametric shapes is often a bottleneck in many applications, thus limiting their interest. In this paper, we propose a learning based SDM generation neural network which is demonstrated on a tridimensional cochlea shape model parameterized by 4 shape parameters. The proposed SDM Neural Network generates a cochlea signed distance map depending on four input parameters and we show that the deep learning approach leads to a 60 fold improvement in the time of computation compared to more classical SDM generation methods. Therefore, the proposed approach achieves a good trade-off between accuracy and efficiency

Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networks

International audienceMetal Artifacts creates often difficulties for a high-quality visual assessment of post-operative imaging in computed tomography (CT). A vast body of methods have been proposed to tackle this issue, but these methods were designed for regular CT scans and their performance is usually insufficient when imaging tiny implants. In the context of post-operative high-resolution CT imaging, we propose a 3D metal artifact reduction algorithm based on a generative adversarial neural network. It is based on the simulation of physically realistic CT metal artifacts created by cochlea implant electrodes on preoperative images. The generated images serve to train 3D generative adversarial networks for artifacts reduction. The proposed approach was assessed qualitatively and quantitatively on clinical conventional and cone-beam CT of cochlear implant postoperative images. These experiments show that the proposed method outperforms other general metal artifact reduction approaches

Automated analysis of human cochlea shape variability from segmented μCT images

International audienceThe 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

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

Optimal electrode diameter in relation to volume of the cochlea

International audienceThe volume of the cochlea is a key parameter for electrode-array design. Indeed, it constrains the diameter of the electrode-array for low-traumatic positioning in the scala timpani. The present report shows a model of scala timpani volume extraction from temporal bones images in order to estimate a maximum diameter of an electrode-array. Nine temporal bones were used, and passed to high-resolution computed tomography scan. Using image-processing techniques, scala timpani were extracted from images, and cross-section areas were estimated along cochlear turns. Cochlear implant electrode-array was fitted in these cross-sections. Results show that the electrode-array diameter is small enough to fit in the scala timpani, however the diameter is restricted at the apical part

Uncertainty Quantification of Cochlear Implant Insertion from CT Images

International audienceCochlear implants (CI) are used to treat severe hearing loss by surgically inserting an electrode array into the cochlea. Since current electrodes are designed with various insertion depth, ENT surgeons must choose the implant that will maximise the insertion depth without causing any trauma based on preoperative CT images. In this paper, we propose a novel framework for estimating the insertion depth and its uncertainty from segmented CT images based on a new parametric shape model. Our method relies on the posterior probability estimation of the model parameters using stochastic sampling and a careful evaluation of the model complexity compared to CT and µCT images. The results indicate that preoperative CT images can be used by ENT surgeons to safely select patient-specific cochlear implants

Impact of the surgical experience on cochleostomy location: a comparative temporal bone study between endaural and posterior tympanotomy approaches for cochlear implantation

International audienceThe goal of this study was to evaluate, in the hands of an inexperienced surgeon, the cochleostomy location of an endaural approach (MINV) compared to the conventional posterior tympanotomy (MPT) approach. Since 2010, we use in the ENT department of Nice a new surgical endaural approach to perform cochlear implantation. In the hands of an inexperienced surgeon, the position of the cochleostomy has not yet been studied in detail for this technique. This is a prospective study of 24 human heads. Straight electrode arrays were implanted by an inexperienced surgeon: on one side using MPT and on the other side using MINV. The cochleostomies were all antero-inferior, but they were performed through an endaural approach with the MINV or a posterior tympanotomy approach with the MPT. The positioning of the cochleostomies into the scala tympani was evaluated by microdissection. Cochleostomies performed through the endaural approach were well placed into the scala tympani more frequently than those performed through the posterior tympanotomy approach (87.5 and 16.7 %, respectively, p < 0.001). This study highlights the biggest challenge for an inexperienced surgeon to achieve a reliable cochleostomy through a posterior tympanotomy, which requires years of experience. In case of an uncomfortable view through a posterior tympanotomy, an inexperienced surgeon might be able to successfully perform a cochleostomy through an endaural (combined approach) or an extended round window approach in order to avoid opening the scala vestibuli

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

Reconstruction tridimensionnelle et étude de la variabilité anatomique de la cochlée à partir d'images médicales

Cochlear implants (CI) are used to treat hearing loss by surgically inserting an electrode array into the organ of hearing, the cochlea. Pre- and post-operative CT images are used routinely for surgery planning and evaluation of cochlear implantation. However, due to the small size and the complex topology of the cochlea, the anatomical information that can be extracted from the images is limited. The first focus of this work aims at defining automatic image processing methods adapted to the spiral shape of the cochlea to study the cochlear shape variability from high-resolution μCT images. The second focus aims at developing and evaluating a new parametric cochlear shape model. The model is applied to extract patient-specific clinically relevant metrics such as the maximal insertion depth of CI electrode arrays. Thanks to the uncertainty quantification, provided by the model, we can assess the reliability of CT-based segmentation as compared to the ground truth segmentation provided by μCT scans. Finally, the last focus concerns a joint model of the cochlear shape (and its substructures) model and its appearance within a generative probabilistic Bayesian framework. The proposed segmentation method was applied to a large database of 987 CT images and allowed the statistical characterization of the cochlear anatomical variability along with the quantification of the bilateral symmetry. This work paves the way to novel clinical applications such as improved diagnosis by identifying pathological cochlear shapes; preoperative optimal electrode design and insertion axis planning; postoperative electrode position estimation and implantation evaluation; and cochlear implantation simulation.Les implants cochléaires (IC) sont utilisés pour traiter la surdité profonde en insérant chirurgicalement un réseau d'électrodes dans l'organe de l'audition, la cochlée. Les images tomodensitométriques (TDM) pré et post-opératoires sont utilisées couramment pour la planification chirurgicale et l'évaluation de l'implantation cochléaire. Cependant, en raison de la petite taille et de la topologie complexe de la cochlée, l'information anatomique qui peut être extraite des images est limitée. Le premier axe de ce travail vise à définir des méthodes automatiques de traitement d'images adaptées à la forme en spirale de la cochlée pour étudier en étudier la variabilité à partir d'images de micro-TDM (μTDM) haute résolution. Le deuxième axe vise à développer et à évaluer un nouveau modèle paramétrique de forme cochléaire. Le modèle est appliqué pour extraire des paramètres cliniquement pertinents spécifiques au patient, tels que la profondeur d'insertion maximale des portes électrode. Grâce à la quantification de l'incertitude, fournie par le modèle, la fiabilité des segmentations issues de TDM a pu être évaluée par rapport à la vérité terrain fournie par μTDM. Enfin, le dernier axe concerne un modèle de forme cochléaire (et de ses sous-structures) et d'apparence combiné dans un cadre bayésien probabiliste génératif. La méthode de segmentation proposée a été appliquée à une grande base de données de 987 images de TDM et a permis la caractérisation statistique de la variabilité anatomique cochléaire ainsi que la quantification de la symétrie bilatérale. Ce travail ouvre la voie à de nouvelles applications cliniques telles que l'amélioration du diagnostic en identifiant les formes cochléaires pathologiques ; la planification préopératoire du choix de l'électrode et de l'axe d'insertion ; l'estimation postopératoire de la position de l'électrode et évaluation de l'implantation ; et la simulation d'implantation cochléaire