  Deep learning has been shown to produce state of the art results in many
tasks in biomedical imaging, especially in segmentation. Moreover, segmentation
of the cerebrovascular structure from magnetic resonance angiography is a
challenging problem because its complex geometry and topology have a large
inter-patient variability. Therefore, in this work, we present a convolutional
neural network approach for this problem. Particularly, a new network topology
inspired by the U-net 3D and by the Inception modules, entitled Uception. In
addition, a discussion about the best objective function for sparse data also
guided most choices during the project. State of the art models are also
implemented for a comparison purpose and final results show that the proposed
architecture has the best performance in this particular context.
