Robust automated constellation-based landmark detection in human brain imaging.
Robust automated constellation-based landmark detection in human brain imaging.
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DOI：
10.1016/j.neuroimage.2017.04.012
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发表时间：
2018-04-15
期刊：
影响因子：
5.7
通讯作者：
Johnson HJ
中科院分区：
文献类型：
--
作者：
Ghayoor A;Vaidya JG;Johnson HJ
描述并验证了一种用于识别人脑中任意数量的标志点的鲁棒的全自动算法。所提出的方法结合统计形状模型与训练的脑形态测量的措施，可靠和准确地估计中脑标志位置。由自动识别的眼睛中心和头部质量的中心提供的总的形态测量约束示出在初始头部取向中存在大旋转的情况下提供鲁棒的初始化。初级中脑标志的检测被用作基础，从该基础通过应用线性模型估计和主成分分析扩展检测在不同脑区域中的任意一组次级标志。该估计模型顺序地使用每个附加的检测到的地标的知识作为下一地标位置的改进预测的改进基础。所提出的方法的准确性和鲁棒性进行了评估，通过比较自动生成的结果，两个手动评分30个识别的标志点提取的30个T1加权磁共振图像。对于具有明确解剖定义的标志，当根据从与模型构建数据相同的部位收集的成像数据评估算法时，算法结果与每个人类观察者之间的平均差异与平均观察者间变异性相差不到1 mm。当将相同的模型应用于来自代表3个扫描仪制造商的7个不同采集地点的一组异质图像体积时，获得了类似的结果。这种方法是可靠的一般应用在大规模的多站点的研究，包括各种成像数据与不同的方向，间距，起源，和场强。
A robust fully automated algorithm for identifying an arbitrary number of landmark points in the human brain is described and validated. The proposed method combines statistical shape models with trained brain morphometric measures to estimate midbrain landmark positions reliably and accurately. Gross morphometric constraints provided by automatically identified eye centers and the center of the head mass are shown to provide robust initialization in the presence of large rotations in the initial head orientation. Detection of primary midbrain landmarks are used as the foundation from which extended detection of an arbitrary set of secondary landmarks in different brain regions by applying a linear model estimation and principle component analysis. This estimation model sequentially uses the knowledge of each additional detected landmark as an improved foundation for improved prediction of the next landmark location. The accuracy and robustness of the presented method was evaluated by comparing the automatically generated results to two manual raters on 30 identified landmark points extracted from each of 30 T1-weighted magnetic resonance images. For the landmarks with unambiguous anatomical definitions, the average discrepancy between the algorithm results and each human observer differed by less than 1 mm from the average inter-observer variability when the algorithm was evaluated on imaging data collected from the same site as the model building data. Similar results were obtained when the same model was applied to a set of heterogeneous image volumes from seven different collection sites representing 3 scanner manufacturers. This method is reliable for general application in large-scale multi-site studies that consist of a variety of imaging data with different orientations, spacings, origins, and field strengths.
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