Measuring size and shape of the hippocampus in MR images using a deformable shape model
Measuring size and shape of the hippocampus in MR images using a deformable shape model
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DOI：
10.1006/nimg.2001.0987
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发表时间：
2002-02-01
期刊：
影响因子：
5.7
通讯作者：
Davatzikos, C
中科院分区：
文献类型：
--
作者：
Shen, DG;Moffat, S;Davatzikos, C
提出了一种基于自动图像分析算法的海马体形状和大小的分割及量化方法。该算法使用可变形形状模型在磁共振图像中定位海马体，并确定其边界的几何表示。可变形模型结合了三类信息。首先，它利用从局部相对较精细尺度到更全局相对较粗糙尺度的海马体边界几何特性信息。其次，该模型包括个体间正常形状变化的统计特征，作为算法的先验知识。第三，该算法利用一些手动定义的边界点，无论这些边界在磁共振图像中是模糊的还是未清晰定义的，都有助于引导模型变形到合适的边界。经过良好训练的评估者所做的手动分割与该算法之间显示出极好的一致性，相关系数等于0.97，并且算法与评估者之间的差异在统计上等同于手动定义的评估者之间的差异。(C) 2002爱思唯尔科学出版社。
A method for segmentation and quantification of the shape and size of the hippocampus is proposed, based on an automated image analysis algorithm. The algorithm uses a deformable shape model to locate the hippocampus in magnetic resonance images and to determine a geometric representation of its boundary. The deformable model combines three types of information. First, it employs information about the geometric properties of the hippocampal boundary, from a local and relatively finer scale to a more global and relatively coarser scale. Second, the model includes a statistical characterization of normal shape variation across individuals, serving as prior knowledge to the algorithm. Third, the algorithm utilizes a number of manually defined boundary points, which can help guide the model deformation to the appropriate boundaries, wherever these boundaries are weak or not clearly defined in MR images. Excellent agreement is demonstrated between the algorithm and manual segmentations by well-trained raters, with a correlation coefficient equal to 0.97 and algorithm/rater differences statistically equivalent to interrater differences for manual definitions. (C) 2002 Elsevier Science.