Segmentation and study of anatomical variability of the cochlea from medical images

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.