Automated algorithm for atlas-based segmentation of the heart and pericardium from non-contrast CT.
Automated algorithm for atlas-based segmentation of the heart and pericardium from non-contrast CT.
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DOI：
10.1117/12.844810
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发表时间：
2010-03-01
期刊：
影响因子：
--
通讯作者：
Berman DS
中科院分区：
文献类型：
--
作者：
Dey D;Ramesh A;Slomka PJ;Nakazato R;Cheng VY;Germano G;Berman DS
从非增强CT中自动分割3D心脏区域是自动量化冠状动脉钙化和心包脂肪的先决条件。我们的目标是开发和验证一种基于图谱的自动、高效的算法，用于从非增强CT中分割心脏和心包。首先从多个手动分割的非对比CT数据创建共同配准的非对比CT图谱。包括在图集中的非对比度CT数据使用迭代仿射配准彼此共同配准，随后使用迭代Demons算法进行可变形变换；最后的变换也被应用于分段的掩模。通过首先共同配准到图集图像，并通过使用应用于所有共同配准/预分割的图集图像的加权决策函数的体素分类来分割新的CT数据集。将这种自动分割方法应用于12个CT数据集，并由8个数据集创建了一个共同配准的图谱。将算法性能与专家手动量化进行了比较。用该算法计算的心脏区域体积(609.0±39.8cc)与专家(624.4±38.4cc)比较，差异无统计学意义(p=0.10，平均百分比差3.8±3.0%)，相关良好(r=0.98p<；0.0001)。该算法获得了0.89(范围0.86-0.91)的平均体素重叠。在标准Windows计算机(100次迭代)上，总时间为45秒。基于图谱的快速、稳健的自动分割心脏和心包从非增强CT是可行的。
Automated segmentation of the 3D heart region from non-contrast CT is a pre-requisite for automated quantification of coronary calcium and pericardial fat. We aimed to develop and validate an automated, efficient atlas-based algorithm for segmentation of the heart and pericardium from non-contrast CT. A co-registered non-contrast CT atlas is first created from multiple manually segmented non-contrast CT data. Non-contrast CT data included in the atlas are co-registered to each other using iterative affine registration, followed by a deformable transformation using the iterative demons algorithm; the final transformation is also applied to the segmented masks. New CT datasets are segmented by first co-registering to an atlas image, and by voxel classification using a weighted decision function applied to all co-registered/pre-segmented atlas images. This automated segmentation method was applied to 12 CT datasets, with a co-registered atlas created from 8 datasets. Algorithm performance was compared to expert manual quantification. Cardiac region volume quantified by the algorithm (609.0 ± 39.8 cc) and the expert (624.4 ± 38.4 cc) were not significantly different (p=0.1, mean percent difference 3.8 ± 3.0%) and showed excellent correlation (r=0.98, p<0.0001). The algorithm achieved a mean voxel overlap of 0.89 (range 0.86–0.91). The total time was <45 sec on a standard windows computer (100 iterations). Fast robust automated atlas-based segmentation of the heart and pericardium from non-contrast CT is feasible.
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影响因子：
6.7
作者：
Dey, Damini;Suzuki, Yasuyuki;Berman, Daniel S.
通讯作者：
Berman, Daniel S.
影响因子：
37.8
作者：
Rosito, Guido A.;Massaro, Joseph M.;Fox, Caroline S.
通讯作者：
Fox, Caroline S.
影响因子：
10.9
作者：
Thirion, J P
通讯作者：
Thirion, J P
DOI：
10.1161/atvbaha.108.180653
发表时间：
2009-05-01
影响因子：
8.7
作者：
Greif, Martin;Becker, Alexander;Leber, Alexander W.
通讯作者：
Leber, Alexander W.