The enhancement and detection of elongated structures in noisy image data is relevant for many biomedical applications. To handle complex crossing structures in 2D images, 2D orientation scores U : R2 × S1 → R were introduced, which already showed their use in a variety of applications. Here we extend this work to 3D orientation scores U : R3 × S2 → R. First, we construct the orientation score from a given dataset, which is achieved by an invertible coherent state type of transform. For this transformation we introduce 3D versions of the 2D cake-wavelets, which are complex wavelets that can simultaneously detect oriented structures and oriented edges. For efficient implementation of the different steps in the wavelet creation we use a spherical harmonic transform. Finally, we show some first results of practical applications of 3D orientation scores. Key words: Orientation Scores, Reproducing Kernel Spaces, 3D Wavelet Design, Scale Spaces on SE(3), Coherence Enhancing Diffusio...