To assess the quality of insertion of Cochlear Implants (CI) after surgery, it is important to analyze the positions of the electrodes with respect to the cochlea based on post-operative CT imaging. Yet, these images suffer from metal artifacts which often entail a difficulty to make any analysis. In this work, we propose a 3D metal artifact reduction method using convolutional neural networks for post-operative cochlear implant imaging. Our approach is based on a 3D generative adversarial network (MARGANs) to create an image with a reduction of metal artifacts. The generative model is trained on a large number of pre-operative "artifact-free" images on which simulated metal artifacts are created. This simulation involves the segmentation of the scala tympani, the virtual insertion of electrode arrays and the simulation of beam hardening based on the Beer-Lambert law. Quantitative and qualitative evaluations compared with two classical metallic artifact reduction algorithms show the effectiveness of our method.

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.

Landmarks detection in a medical image is a mainstay for many clinical algorithms application. Learning-based landmarks detection is now a major successful methodology for many types of objects detection. However, learningbased approaches usually need a number of the annotated dataset for training the learning models. To tackle the lack of annotation issue, in this work, an automatic one-shot learning-based landmarks detection approach is proposed for identifying the landmarks in 3D volume images. A convolutional neural network-based iterative objects localization method in combine with a registration framework is applied for automatically target organ localization and landmarks matching. We investigated both qualitatively and quantitatively the performance of the proposed approach on clinical temporal bone CT volumes. The result shows that the proposed method is robust in convergence, effective in accuracy and reliable for clinical usage.

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Metal 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 a 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.