Dey Damini, Ramesh Amit, Slomka Piotr J, Nakazato Ryo, Cheng Victor Y, Germano Guido, Berman Daniel S
Departments of Imaging and Medicine, Cedars Sinai Medical Center, Los Angeles, CA.
Proc SPIE Int Soc Opt Eng. 2010 Mar 1;7623:762337. doi: 10.1117/12.844810.
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.
从非增强CT中自动分割3D心脏区域是自动定量冠状动脉钙化和心包脂肪的前提条件。我们旨在开发并验证一种基于图谱的自动高效算法，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增强CT数据创建一个配准的非增强CT图谱。图谱中包含的非增强CT数据使用迭代仿射配准相互配准，然后使用迭代 demons 算法进行可变形变换；最终变换也应用于分割掩码。新的CT数据集通过首先与图谱图像配准，并使用应用于所有配准/预分割图谱图像的加权决策函数进行体素分类来进行分割。这种自动分割方法应用于12个CT数据集，使用8个数据集创建了一个配准图谱。将算法性能与专家手动定量进行比较。算法量化的心脏区域体积（609.0±39.8 cc）与专家量化的结果（624.4±38.4 cc）无显著差异（p = 0.1，平均百分比差异3.8±3.0%），且显示出极好的相关性（r = 0.98，p < 0.0001）。该算法的平均体素重叠率为0.89（范围0.86 - 0.91）。在标准Windows计算机上（100次迭代）总时间<45秒。从非增强CT中快速稳健地基于图谱自动分割心脏和心包是可行的。
