In this paper we demonstrate the effectiveness of reference (or atlas)-based non-rigid registration to the segmentation of medical and biological imagery. In particular we introduce a segmentation functional exploiting feature information about the reference image and we minimize it with respect to the parameters of the non-rigid transformation, akin to a region based maximum likelihood estimation process. The warping transformation is modeled using Thin Plate Splines, which incorporate information about the global rigid motion and the non-rigid local displacements. Extensive experimental evaluations and comparisons with other segmentation techniques on a complex biological dataset are presented. The proposed algorithm outperforms the others in both classification rate and, in particular, localization accuracy.