Automatic Construction of Statistical Shape Models for Vertebrae

Abstract

Abstract. For segmenting complex structures like vertebrae, a priori knowledge by means of statistical shape models (SSMs) is often incorporated. One of the main challenges using SSMs is the solution of the correspondence problem. In this work we present a generic automated approach for solving the correspondence problem for vertebrae. We determine two closed loops on a reference shape and propagate them consistently to the remaining shapes of the training set. Then every shape is cut along these loops and parameterized to a rectangle. There, we optimize a novel combined energy to establish the correspondences and to reduce the unavoidable area and angle distortion. Finally, we present an adaptive resampling method to achieve a good shape representation. A qualitative and quantitative evaluation shows that using our method we can generate SSMs of higher quality than the ICP approach.

Dual-energy CT-based Assessment of the Trabecular Bone in Vertebrae

“…For this correspondence establishment, we use the parameterization-based approach recently introduced by Becker et al [15]. We first choose a reference mesh which is cut along two loops -using the algorithm of Erickson and Whittlesey [16] -in order to be able to flatten it to a rectangle.…”