Three-Dimensional (3D) Active Shape Modeling (ASM) is a straightforward extension of 2D ASM. 3D ASM is robust when true volumetric data is considered. However, when the information in one dimension is sparse, pure 3D ASM tends to be less robust. We present a hybrid 2D + 3D methodology which can deal with sparse 3D data. 2D and 3D ASMs are combined to obtain a 'global optimal' segmentation of the 3D object embedded in the data set, rather than the 'locally optimal' segmentation on separate slices. Experimental results indicate that the developed approach shows equivalent precision on separate slices but higher consistency for whole volumes when compared to 2D ASM, while the results for whole volumes are improved when compared to the pure 3D ASM approach.