Speier William, Iglesias Juan E, El-Kara Leila, Tu Zhuowen, Arnold Corey
University of California, Los Angeles, USA.
Med Image Comput Comput Assist Interv. 2011;14(Pt 3):659-66. doi: 10.1007/978-3-642-23626-6_81.
Skull stripping is the first step in many neuroimaging analyses and its success is critical to all subsequent processing. Methods exist to skull strip brain images without gross deformities, such as those affected by Alzheimer's and Huntington's disease. However, there are no techniques for extracting brains affected by diseases that significantly disturb normal anatomy. Glioblastoma multiforme (GBM) is such a disease, as afflicted individuals develop large tumors that often require surgical resection. In this paper, we extend the ROBEX skull stripping method to extract brains from GBM images. The proposed method uses a shape model trained on healthy brains to be relatively insensitive to lesions inside the brain. The brain boundary is then searched for potential resection cavities using adaptive thresholding and the Random Walker algorithm corrects for leakage into the ventricles. The results show significant improvement over three popular skull stripping algorithms (BET, BSE and HWA) in a dataset of 48 GBM cases.
颅骨剥离是许多神经影像分析的第一步，其成功与否对所有后续处理至关重要。现有方法可对无明显畸形的脑图像进行颅骨剥离，比如受阿尔茨海默病和亨廷顿病影响的脑图像。然而，对于受严重扰乱正常解剖结构疾病影响的大脑，尚无提取技术。多形性胶质母细胞瘤（GBM）就是这样一种疾病，因为患病个体会长出大肿瘤，通常需要手术切除。在本文中，我们扩展了ROBEX颅骨剥离方法，以从GBM图像中提取大脑。所提出的方法使用在健康大脑上训练的形状模型，使其对脑内病变相对不敏感。然后使用自适应阈值搜索脑边界以查找潜在的切除腔，并使用随机游走算法校正渗入脑室的情况。在48例GBM病例的数据集中，结果显示与三种流行的颅骨剥离算法（BET、BSE和HWA）相比有显著改进。
