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.

翻译：脑表面提取是医疗成像系统的一个基本主题,因为它为神经外科医生提供了更广阔的外科规划和异常检测视角。为了解决目前缺乏先前形状信息的深层学习头骨剥离方法所面临的问题,我们提议建立一个新的网络结构,纳入对已签字的距离场的知识,并引入额外的拉普拉西亚损失,以确保预测结果保留形状信息。我们通过对我们的脑磁共振成像数据集(111名病人)进行实验,验证了我们新提出的方法。评估结果表明,我们的方法取得了相似的骰子分数,并减少了Hausdorff的距离和平均对称表距离,从而产生了更稳定、更顺畅的大脑表层。