  Segmentation of brain arterio-venous malformations (bAVMs) in 3D rotational
angiographies (3DRA) is still an open problem in the literature, with high
relevance for clinical practice. While deep learning models have been applied
for segmenting the brain vasculature in these images, they have never been used
in cases with bAVMs. This is likely caused by the difficulty to obtain
sufficiently annotated data to train these approaches. In this paper we
introduce a first deep learning model for blood vessel segmentation in 3DRA
images of patients with bAVMs. To this end, we densely annotated 5 3DRA volumes
of bAVM cases and used these to train two alternative 3DUNet-based
architectures with different segmentation objectives. Our results show that the
networks reach a comprehensive coverage of relevant structures for bAVM
analysis, much better than what is obtained using standard methods. This is
promising for achieving a better topological and morphological characterisation
of the bAVM structures of interest. Furthermore, the models have the ability to
segment venous structures even when missing in the ground truth labelling,
which is relevant for planning interventional treatments. Ultimately, these
results could be used as more reliable first initial guesses, alleviating the
cumbersome task of creating manual labels.
