Automatic Model Generation Framework for Computational Simulation of Cochlear Implantation

Abstract

Recent developments in computational modeling of cochlear implantation are promising to study in-silico the performance of the implant before surgery. However, creating a complete computational model of the patient's anatomy while including an external device geometry remains challenging. To address such a challenge, we propose an automatic framework for the generation of patient-specific meshes for finite element modeling of the implanted cochlea. First, a statistical shape model is constructed from highresolution anatomical µCT images. Then, by fitting the statistical model to a patient's CT image, an accurate model of the patient-specific cochlea anatomy is obtained. An algorithm based on the parallel transport frame is employed to perform the virtual insertion of the cochlear implant. Our automatic framework also incorporates the surrounding bone and nerve fibers and assigns constitutive parameters to all components of the finite element model. This model can then be used to study in-silico the e↵ects of the electrical stimulation of the cochlear implant. Results are shown on a total of 25 models of patients. In all cases, a final mesh suitable for finite element 1 This a pre-print version. The final document is available at http://www.springerlink.com simulations was obtained, in an average time of 94 seconds. The framework has proven to be fast and robust, and is promising for a detailed prognosis of the cochlear implantation surgery.

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

“…Despite the limited sample size, the resulting segmentations are accurate. Moreover, the same data have been used for further analysis [1][2][3] where it has shown to be representative of the different patient variations. Thus, we believe that the proposed method will generalize well to other data sets.…”

“…The segmentations play a crucial part in the generation of accurate patient-specific computational models, which can guide implant design, insertion planning and selection of the best treatment strategy for each patient [2,3].…”

Multi-region statistical shape model for cochlear implantation

“…5 Further, it also enables the prediction of patient CI designs and outcomes by simulating the implant placement and function over the patient estimated cochlea. 6,7 Global modeling of complex anatomical structures as the inner ear is a difficult task with a standard Point Distribution Model (PDM), 4 since the amount of µCT data is limited. Typically, the global anatomical variability between complex structures of the inner ear is associated to the local variability of their corresponding physiological regions, i.e., the global variability can be explained in a local form.…”

Towards a Complete In Silico Assessment of the Outcome of Cochlear Implantation Surgery

“…In previous studies, we proposed an automatic framework for the generation of computational models of CI [32]. In this work, we further develop this model to compute the final neural response after CI.…”