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

In situ validation of a parametric model of electrical field distribution in an implanted cochlea

International audienceCochlear implants have been proved to be an effective treatment for patients with sensorineural hearing loss. Among all the approaches that have been developed to design better cochlear implants, 3D model-based simulation stands out due to its detailed description of the electric field which helps reveal the electrophysiological phenomena inside the cochlea. With the advances in the cochlear implant manufacturing technology, the requirement on simulation accuracy increases. Improving the simulation accuracy relies on two aspects: 1) a better geometrical description of the cochlea that is able to distinguish the subtle differences across patients; 2) a comprehensive and reliable validation of the created 3D model. In this paper, targeting at high precision simulation, we propose a parametric cochlea model which uses micro-CT images to adapt to different cochlea geometries, then demonstrate its validation process with multi-channel stimulation data measured from a implanted cochlea. Comparisons between the simulation and validation data show a good match under a variety of stimulation configurations. The results suggest that the electric field distribution is affected by the geometric characteristics of each individual cochlea. These differences can be correctly reflected by simulations based on a 3D model tuned with personalized data

Realistic Simulation of Electric Potential Distributions of Different Stimulation Modes in an Implanted Cochlea

International audienceSimulation of the intracochlear potentials is an important approach to study the activation of auditory nerve fibers under electrical stimulations.However, it is still unclear to which extent the simulation results are affected by precision in reproducing the exact cochlear geometry.In this study, we address to this question by comparing the actual electric potential measured from implanted human specimen with the simulationoutputs from two different parametric 3D cochlear models. One of the model is created from the default values[1] while the other is adapted to the micro-CT scan data of the implanted cochlea

Evaluation of the current distribution of the hybrid common ground stimulation in cochlear implants

International audienceBackgroundIn cochlear implants, the hybrid common ground is a combination of a classic monopolarstimulation with a standard common ground. This new stimulation montage allows the current toreturn from both the non-stimulating electrodes on the electrode array and the reference electrodeplaced between the skull and scalp. In theory, this lead to reach a compromise between the currentfocality and the efficiency of the stimulation. Even if this stimulation type has already been adoptedby some implant manufacturers, the 3D geometry of its current pathways remains to be studied.MethodsThe study is two-fold. First, an in-vitro experiment aimed to measure the electrical field producedby the hybrid common ground stimulation. An electrode array of an XP implant (Oticon Medical,Neurelec) was placed in saline solution and the electrical field was recorded by a probe that movesalong the programmed grid. During the stimulation, the current waveforms on all the groundingelectrodes were also recorded. Second, an in-situ measurement was conducted. Another XP devicewas implanted into a human specimen. The same procedure as in the in-vitro measurement wasperformed to record, this time, the current waveforms only.ResultsThe recorded two groups of current data were compared with each other to investigate how thecurrent path is modified by the geometry of a human cochlea. The potential distribution measuredduring the in-vitro experiment was also compared with other stimulation types such as monopolar.The energy consumption of the stimulation was also computed from the collected data

One-shot Learning Landmarks Detection

International audienceLandmark detection in medical images is important for many clinical applications. Learning-based landmark detection is successful at solving some problems but it usually requires a large number of annotated datasets for the training stage. In addition, traditional methodsusually fail for the landmark detection of fine objects. In this paper, we tackle the issue of automatic landmark annotation in 3D volumetricimages from a single example based on a one-shot learning method. It involves the iterative training of a shallow convolutional neural network combined with a 3D registration algorithm in order to perform automatic organ localization and landmark matching. We investigated both qualitatively and quantitatively the performance of the proposed approach on clinical temporal bone CT volumes. The results show that our oneshot learning scheme converges well and leads to a good accuracy of the landmark positions

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

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