Multi-atlas skull-stripping.
Multi-atlas skull-stripping.
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DOI：
10.1016/j.acra.2013.09.010
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发表时间：
2013-12
影响因子：
4.8
通讯作者：
Davatzikos, Christos
中科院分区：
文献类型：
--
作者：
Doshi, Jimit;Erus, Guray;Ou, Yangming;Gaonkar, Bilwaj;Davatzikos, Christos
提出了一种基于多图谱配准框架的结构磁共振图像脑自动提取新方法。我们的方法解决了多图谱方法的根本挑战。为了克服研究之间的成像特征的变异性所产生的困难，我们提出了一个研究特定的模板选择策略，通过该策略，我们选择一组最能代表数据集内的解剖变异的模板。针对用颅骨配准大脑图像的困难，我们使用了一种特别适应的配准算法，该算法对图像之间的大变化更鲁棒，因为它不仅基于它们的相似性，而且基于图像之间匹配的可靠性，自适应地对齐两个图像的不同区域。最后，一个空间自适应加权投票策略，它使用雅可比行列式值的排名来衡量模板和目标图像之间的局部相似性，适用于结合配准模板掩模。该方法在三个不同的公共数据集上进行了验证，并获得了比最近最先进的大脑提取方法更高的准确性。此外，所提出的方法成功地应用于最近的几个成像研究，每个研究包含数千个磁共振图像，从而大大减少了手动校正时间。新方法作为一个独立的软件包供公众使用，提供了一个强大而准确的大脑提取工具，适用于临床使用和大规模人群研究。
We present a new method for automatic brain extraction on structural magnetic resonance images, based on a multi-atlas registration framework. Our method addresses fundamental challenges of multi-atlas approaches. To overcome the difficulties arising from the variability of imaging characteristics between studies, we propose a study-specific template selection strategy, by which we select a set of templates that best represent the anatomical variations within the data set. Against the difficulties of registering brain images with skull, we use a particularly adapted registration algorithm that is more robust to large variations between images, as it adaptively aligns different regions of the two images based not only on their similarity but also on the reliability of the matching between images. Finally, a spatially adaptive weighted voting strategy, which uses the ranking of Jacobian determinant values to measure the local similarity between the template and the target images, is applied for combining coregistered template masks. The method is validated on three different public data sets and obtained a higher accuracy than recent state-of-the-art brain extraction methods. Also, the proposed method is successfully applied on several recent imaging studies, each containing thousands of magnetic resonance images, thus reducing the manual correction time significantly. The new method, available as a stand-alone software package for public use, provides a robust and accurate brain extraction tool applicable for both clinical use and large population studies.
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影响因子：
10.6
作者：
Warfield, SK;Zou, KH;Wells, WM
通讯作者：
Wells, WM
简单的范式去除外部组织：算法和分析。
影响因子：
5.7
作者：
Carass, Aaron;Cuzzocreo, Jennifer;Wheeler, M. Bryan;Bazin, Pierre-Louis;Resnick, Susan M.;Prince, Jerry L.
通讯作者：
Prince, Jerry L.
影响因子：
4.8
作者：
Smith, SM
通讯作者：
Smith, SM
影响因子：
10.9
作者：
Ou, Yangming;Sotiras, Aristeidis;Paragios, Nikos;Davatzikos, Christos
通讯作者：
Davatzikos, Christos
影响因子：
5.7
作者：
Shattuck, DW;Sandor-Leahy, SR;Leahy, RM
通讯作者：
Leahy, RM