  Intra-operative ultrasound is an increasingly important imaging modality in
neurosurgery. However, manual interaction with imaging data during the
procedures, for example to select landmarks or perform segmentation, is
difficult and can be time consuming. Yet, as registration to other imaging
modalities is required in most cases, some annotation is necessary. We propose
a segmentation method based on DeepVNet and specifically evaluate the
integration of pre-training with simulated ultrasound sweeps to improve
automatic segmentation and enable a fully automatic initialization of
registration. In this view, we show that despite training on coarse and
incomplete semi-automatic annotations, our approach is able to capture the
desired superficial structures such as \textit{sulci}, the \textit{cerebellar
tentorium}, and the \textit{falx cerebri}. We perform a five-fold
cross-validation on the publicly available RESECT dataset. Trained on the
dataset alone, we report a Dice and Jaccard coefficient of $0.45 \pm 0.09$ and
$0.30 \pm 0.07$ respectively, as well as an average distance of $0.78 \pm
0.36~mm$. With the suggested pre-training, we computed a Dice and Jaccard
coefficient of $0.47 \pm 0.10$ and $0.31 \pm 0.08$, and an average distance of
$0.71 \pm 0.38~mm$. The qualitative evaluation suggest that with pre-training
the network can learn to generalize better and provide refined and more
complete segmentations in comparison to incomplete annotations provided as
input.
