Generating shapes by analogies: An application to hearing aid design

3D shape modeling is a crucial component of rapid prototyping systems that customize shapes of implants and prosthetic devices to a patient’s anatomy. In this paper, we present a solution to the problem of customized 3D shape modeling using a statistical shape analysis framework. We design a novel method to learn the relationship between two classes of shapes, which are related by certain operations or transformation. The two associated shape classes are represented in a lower dimensional manifold, and the reduced set of parameters obtained in this subspace is utilized in an estimation, which is exemplified by a multivariate regression in this paper. We demonstrate our method with a felicitous application to the estimation of customized hearing aid devices.

Research highlights
► From a reference input anatomical shape, a target output anatomical shape customized to a person is estimated by relating the two shape classes on a low dimensional linear manifold by a transform obtained through a multivariate regression.
► In a specic application, a hearing aid shape is automatically estimated from a patient’s raw ear impression using the constructed transform.
► Further refinement of the shape is achieved using an auxiliary shape class, which is obtained from the difference of the input and output shapes.
► A hybrid solution that includes interactions to modify the estimated cutting planes can be provided as an additional flexibility of the proposed framework, which contains separable components.