Segmentation of brain magnetic resonance images based on multi-atlas likelihood fusion: testing using data with a broad range of anatomical and photometric profiles.
Segmentation of brain magnetic resonance images based on multi-atlas likelihood fusion: testing using data with a broad range of anatomical and photometric profiles.
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DOI：
10.3389/fnins.2015.00061
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发表时间：
2015
影响因子：
4.3
通讯作者：
Miller MI
中科院分区：
文献类型：
--
作者：
Tang X;Crocetti D;Kutten K;Ceritoglu C;Albert MS;Mori S;Mostofsky SH;Miller MI
我们提出了一种分层管道，用于从人脑 T1 加权图像中剥离和分割感兴趣的解剖结构。该管道是基于称为多图集似然融合（MALF）的两级贝叶斯参数估计算法构建的。在 MALF 中，感兴趣参数的估计是使用期望最大化 (EM) 算法通过最大后验估计来执行的。多个图集的似然在 E 步骤中融合，而最优估计器（融合似然的单个最大化器）随后在 M 步骤中获得。拟议的管道有两个阶段；首先，通过快速 MALF 自动剥离输入的 T1 加权图像，然后使用常规 MALF 自动提取感兴趣的内部大脑结构。我们根据解剖学和光度对比明显不同的两组图像来评估管道中两个模块的性能；患有发育障碍的儿科受试者的 3T MPRAGE 扫描与患有痴呆症的老年受试者的 1.5T SPGR 扫描。使用 Dice 重叠进行定量评估，并通过目视检查进行定性评估。因此，我们证明了所提出的管道性能的受试者水平差异，这可能是由年龄、诊断或成像参数（特别是场强）造成的。对于两个数据集的皮层下和心室结构，分层管道能够生成自动分割，与黄金标准相比，Dice 重叠范围为 0.8 到 0.964。与其他代表性分割算法进行了比较，相对于此，所提出的分层管道表现出相当或更高的准确性。
We propose a hierarchical pipeline for skull-stripping and segmentation of anatomical structures of interest from T1-weighted images of the human brain. The pipeline is constructed based on a two-level Bayesian parameter estimation algorithm called multi-atlas likelihood fusion (MALF). In MALF, estimation of the parameter of interest is performed via maximum a posteriori estimation using the expectation-maximization (EM) algorithm. The likelihoods of multiple atlases are fused in the E-step while the optimal estimator, a single maximizer of the fused likelihoods, is then obtained in the M-step. There are two stages in the proposed pipeline; first the input T1-weighted image is automatically skull-stripped via a fast MALF, then internal brain structures of interest are automatically extracted using a regular MALF. We assess the performance of each of the two modules in the pipeline based on two sets of images with markedly different anatomical and photometric contrasts; 3T MPRAGE scans of pediatric subjects with developmental disorders vs. 1.5T SPGR scans of elderly subjects with dementia. Evaluation is performed quantitatively using the Dice overlap as well as qualitatively via visual inspections. As a result, we demonstrate subject-level differences in the performance of the proposed pipeline, which may be accounted for by age, diagnosis, or the imaging parameters (particularly the field strength). For the subcortical and ventricular structures of the two datasets, the hierarchical pipeline is capable of producing automated segmentations with Dice overlaps ranging from 0.8 to 0.964 when compared with the gold standard. Comparisons with other representative segmentation algorithms are presented, relative to which the proposed hierarchical pipeline demonstrates comparative or superior accuracy.
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影响因子：
5.7
作者：
Patenaude, Brian;Smith, Stephen M.;Kennedy, David N.;Jenkinson, Mark
通讯作者：
Jenkinson, Mark
DOI：
10.1023/b:visi.0000043755.93987.aa
发表时间：
2005-02-01
影响因子：
19.5
作者：
Beg, MF;Miller, MI;Younes, L
通讯作者：
Younes, L
影响因子：
10.6
作者：
Sled, JG;Zijdenbos, AP;Evans, AC
通讯作者：
Evans, AC
DOI：
10.1073/pnas.90.24.11944
发表时间：
1993-12-15
影响因子：
11.1
作者：
MILLER, MI;CHRISTENSEN, GE;GRENANDER, U
通讯作者：
GRENANDER, U
影响因子：
10.9
作者：
Baillard, C;Hellier, P;Barillot, C
通讯作者：
Barillot, C