  Whole-brain surface extraction is an essential topic in medical imaging
systems as it provides neurosurgeons with a broader view of surgical planning
and abnormality detection. To solve the problem confronted in current deep
learning skull stripping methods lacking prior shape information, we propose a
new network architecture that incorporates knowledge of signed distance fields
and introduce an additional Laplacian loss to ensure that the prediction
results retain shape information. We validated our newly proposed method by
conducting experiments on our brain magnetic resonance imaging dataset (111
patients). The evaluation results demonstrate that our approach achieves
comparable dice scores and also reduces the Hausdorff distance and average
symmetric surface distance, thus producing more stable and smooth brain
isosurfaces.
