A hybrid approach to the skull stripping problem in MRI
A hybrid approach to the skull stripping problem in MRI
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DOI：
10.1016/j.neuroimage.2004.03.032
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发表时间：
2004-07-01
期刊：
影响因子：
5.7
通讯作者：
Fischl, B
中科院分区：
文献类型：
--
作者：
Ségonne, F;Dale, AM;Fischl, B
提出了一种新的基于分水岭算法和变形曲面模型相结合的颅骨剥离算法。我们的方法利用了前者的稳健性，以及后者可用的表面信息。该算法首先在T1加权的MRI图像中定位单个白质体素，并使用它在白质中创建全局最小值，然后应用具有预泛洪高度的分水岭算法。分水岭算法基于白质的三维连通性建立了对大脑体积的初步估计。这第一步是稳健的，在强度不均匀和噪声存在的情况下表现良好，但可能会侵蚀与明亮的非大脑结构(如眼窝)相邻的部分皮质，或者可能会移除部分小脑。为了纠正这些不准确，表面变形过程适合平滑的表面到被遮盖的体积，允许在颅骨剥离过程中加入几何约束。由一组准确分割的大脑生成的统计图谱被用于验证和潜在地校正分割，并且在大脑边界处局部地重新估计MRI强度值。最后，进行高分辨率的表面变形，精确匹配大脑的外部边界，从而产生健壮和自动化的过程。我们团队和其他人的研究结果超过了其他公开提供的头骨剥离工具。(C)2004 Elsevier Inc.保留所有权利。
We present a novel skull-stripping algorithm based on a hybrid approach that combines watershed algorithms and deformable surface models. Our method takes advantage of the robustness of the former as well as the surface information available to the latter. The algorithm first localizes a single white matter voxel in a T1-weighted MRI image, and uses it to create a global minimum in the white matter before applying a watershed algorithm with a preflooding height. The watershed algorithm builds an initial estimate of the brain volume based on the three-dimensional connectivity of the white matter. This first step is robust, and performs well in the presence of intensity nonuniformities and noise, but may erode parts of the cortex that abut bright nonbrain structures such as the eye sockets, or may remove parts of the cerebellum. To correct these inaccuracies, a surface deformation process fits a smooth surface to the masked volume, allowing the incorporation of geometric constraints into the skull-stripping procedure. A statistical atlas, generated from a set of accurately segmented brains, is used to validate and potentially correct the segmentation, and the MRI intensity values are locally re-estimated at the boundary of the brain. Finally, a high-resolution surface deformation is performed that accurately matches the outer boundary of the brain, resulting in a robust and automated procedure. Studies by our group and others outperform other publicly available skull-stripping tools. (C) 2004 Elsevier Inc. All rights reserved.