Brain Surface Reconstruction from MRI Images Based on Segmentation Networks Applying Signed Distance Maps
This work addresses the need for more stable and smooth brain isosurfaces in medical imaging for neurosurgeons, though it appears incremental as it builds on existing deep learning skull stripping methods.
The paper tackles the problem of brain surface reconstruction from MRI images by proposing a network that incorporates signed distance fields and a Laplacian loss to retain shape information, achieving comparable dice scores while reducing Hausdorff distance and average symmetric surface distance on a dataset of 111 patients.
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.