Automated Multiscale 3D Feature Learning for Vessels Segmentation in Thorax CT Images

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 96.661.10% to 97.240.90%.