Skull stripping magnetic resonance images (MRI) of the human brain is an important process in many image processing techniques, such as automatic segmentation of brain structures. Numerous methods have been developed to perform this task, however, they often fail in the presence of neuropathology and can be inconsistent in defining the boundary of the brain mask. Here, we propose a novel approach to skull strip T1-weighted images in a robust and efficient manner, aiming to consistently segment the outer surface of the brain, including the sulcal cerebrospinal fluid (CSF), while excluding the full extent of the subarachnoid space and meninges. We train a modified version of the U-net on silver-standard ground truth data using a novel loss function based on the signed-distance transform (SDT). We validate our model both qualitatively and quantitatively using held-out data from the training dataset, as well as an independent external dataset. The brain masks used for evaluation partially or fully include the subarachnoid space, which may introduce bias into the comparison; nonetheless, our model demonstrates strong performance on the held-out test data, achieving a consistent mean Dice similarity coefficient (DSC) of 0.964$\pm$0.006 and an average symmetric surface distance (ASSD) of 1.4mm$\pm$0.2mm. Performance on the external dataset is comparable, with a DSC of 0.958$\pm$0.006 and an ASSD of 1.7$\pm$0.2mm. Our method achieves performance comparable to or better than existing state-of-the-art methods for brain extraction, particularly in its highly consistent preservation of the brain's outer surface. The method is publicly available on GitHub.

翻译：人脑磁共振成像（MRI）的颅骨剥离是许多图像处理技术（如脑结构自动分割）中的重要步骤。尽管已有多种方法被开发用于执行此任务，但这些方法在存在神经病理学改变时常常失效，且在定义脑组织掩模边界时缺乏一致性。本文提出一种新颖的方法，以鲁棒且高效的方式对T1加权图像进行颅骨剥离，旨在一致地分割脑组织外表面（包括脑沟脑脊液），同时完全排除蛛网膜下腔和脑膜。我们使用基于符号距离变换（SDT）的新型损失函数，在银标准地面真值数据上训练了一个改进版本的U-net模型。我们使用训练数据集中的预留数据以及独立的外部数据集，对模型进行了定性和定量验证。用于评估的脑组织掩模部分或完全包含了蛛网膜下腔，这可能给比较带来偏差；尽管如此，我们的模型在预留测试数据上表现出色，取得了0.964$\pm$0.006的稳定平均Dice相似系数（DSC）和1.4mm$\pm$0.2mm的平均对称表面距离（ASSD）。在外部数据集上的性能相当，DSC为0.958$\pm$0.006，ASSD为1.7$\pm$0.2mm。本方法在脑组织提取方面达到或超越了现有最先进方法的性能，特别是在高度一致地保留脑组织外表面方面表现突出。该方法已在GitHub上公开提供。