Cerebrovascular Network Segmentation on MRA Images with Deep Learning

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 inspired by the U-net 3D and by the Inception modules, entitled Uception. State of the art models are implemented for a comparison purpose and final results show that the proposed architecture has the best performance in this particular context.