  We address the vessel segmentation problem by building upon the multiscale
feature learning method of Kiros et al., which achieves the current top score
in the VESSEL12 MICCAI challenge. Following their idea of feature learning
instead of hand-crafted filters, we have extended the method to learn 3D
features. The features are learned in an unsupervised manner in a multi-scale
scheme using dictionary learning via least angle regression. The 3D feature
kernels are further convolved with the input volumes in order to create feature
maps. Those maps are used to train a supervised classifier with the annotated
voxels. In order to process the 3D data with a large number of filters a
parallel implementation has been developed. The algorithm has been applied on
the example scans and annotations provided by the VESSEL12 challenge. We have
compared our setup with Kiros et al. by running their implementation. Our
current results show an improvement in accuracy over the slice wise method from
96.66$\pm$1.10% to 97.24$\pm$0.90%.
