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Whole-brain surface extraction is an essential topic in medical imagingsystems as it provides neurosurgeons with a broader view of surgical planningand abnormality detection. To solve the problem confronted in current deeplearning skull stripping methods lacking prior shape information, we propose anew network architecture that incorporates knowledge of signed distance fieldsand introduce an additional Laplacian loss to ensure that the predictionresults retain shape information. We validated our newly proposed method byconducting experiments on our brain magnetic resonance imaging dataset (111patients). The evaluation results demonstrate that our approach achievescomparable dice scores and also reduces the Hausdorff distance and averagesymmetric surface distance, thus producing more stable and smooth brainisosurfaces.

Brain Surface Reconstruction from MRI Images Based on Segmentation Networks Applying Signed Distance Maps