Grabowski T J, Frank R J, Szumski N R, Brown C K, Damasio H
Department of Neurology, Division of Behavioral Neurology and Cognitive Neuroscience, University of Iowa College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, Iowa 52242-1053, USA.
Neuroimage. 2000 Dec;12(6):640-56. doi: 10.1006/nimg.2000.0649.
We describe and evaluate a practical, automated algorithm based on local statistical mixture modeling for segmenting single-channel, T1-weighted volumetric magnetic resonance images of the brain into gray matter, white matter, and cerebrospinal fluid. We employed a stereological sampling method to assess, prospectively, the performance of the method with respect to human experts on 10 normal T1-weighted brain scans acquired with a three-dimensional gradient echo pulse sequence. The overall kappa statistic for the concordance of the algorithm with the human experts was 0.806, while that among raters, excluding the algorithm, was 0.802. The algorithm had better agreement with the modal expert decision (kappa = 0.878). The algorithm could not be distinguished from the experts by this measure. We also validated the algorithm on a simulated MR scan of a digital brain phantom with known tissue composition. Global gray matter and white matter errors were 1% and <1%, respectively, and correlation coefficients with the underlying tissue model were 0.95 for gray matter, 0.98 for white matter, and 0.95 for cerebrospinal fluid. In both approaches to validation, we evaluated both local and global performance of the algorithm. Human experts generated slightly higher global gray matter proportion estimates on the test brain scans relative to the algorithm (3.7%) and on the simulated MR scan relative to the true tissue model (4.4%). The algorithm underestimated gray in some subcortical nuclei which contain admixed gray and white matter. We demonstrate the reliability of the method on individual 1 NEX data sets of the test subjects, and its insensitivity to the precise values of initial model parameters. The output of this algorithm is suitable for quantifying cerebral cortical tissue, using a commonly performed commercial pulse sequence.
我们描述并评估了一种基于局部统计混合建模的实用自动化算法，该算法用于将单通道T1加权的大脑容积磁共振图像分割为灰质、白质和脑脊液。我们采用了一种体视学采样方法，前瞻性地评估该方法相对于人类专家在10例使用三维梯度回波脉冲序列采集的正常T1加权脑部扫描图像上的性能。该算法与人类专家一致性的总体kappa统计量为0.806，而不包括该算法的评估者之间的kappa统计量为0.802。该算法与多数专家决策的一致性更好（kappa = 0.878）。通过这种测量方法，该算法与专家之间没有明显差异。我们还在具有已知组织组成的数字脑模型的模拟磁共振扫描上验证了该算法。全局灰质和白质误差分别为1%和<1%，与基础组织模型的相关系数对于灰质为0.95，对于白质为0.98，对于脑脊液为0.95。在两种验证方法中，我们都评估了该算法的局部和全局性能。在测试脑部扫描图像上，相对于该算法，人类专家生成的全局灰质比例估计值略高（3.7%）；在模拟磁共振扫描上，相对于真实组织模型，人类专家生成的全局灰质比例估计值略高（4.4%）。该算法在一些包含混合灰质和白质的皮质下核中低估了灰质。我们展示了该方法在测试对象的单个1 NEX数据集上的可靠性，以及它对初始模型参数精确值的不敏感性。该算法的输出适用于使用常用的商业脉冲序列对脑皮质组织进行量化。
