Quantitative Imaging Group, Faculty of Applied Sciences, Delft University of Technology, 2628 CJ Delft, The Netherlands.
Med Phys. 2013 Sep;40(9):091910. doi: 10.1118/1.4817577.
There is increasing evidence that epicardial fat (i.e., adipose tissue contained within the pericardium) plays an important role in the development of cardiovascular disease. Obtaining the epicardial fat volume from routinely performed non-enhanced cardiac CT scans is therefore of clinical interest. The purpose of this work is to investigate the feasibility of automatic pericardium segmentation and subsequent quantification of epicardial fat on non-enhanced cardiac CT scans.
Imaging data of 98 randomly selected subjects belonging to a larger cohort of subjects who underwent a cardiac CT scan at our medical center were retrieved. The data were acquired on two different scanners. Automatic multi-atlas based method for segmenting the pericardium and calculating the epicardial fat volume has been developed. The performance of the method was assessed by (1) comparing the automatically segmented pericardium to a manually annotated reference standard, (2) comparing the automatically obtained epicardial fat volumes to those obtained manually, and (3) comparing the accuracy of the automatic results to the inter-observer variability.
Automatic segmentation of the pericardium was achieved with a Dice similarity index of 89.1 ± 2.6% with respect to Observer 1 and 89.2 ± 1.9% with respect to Observer 2. The correlation between the automatic method and the manual observers with respect to the epicardial fat volume computed as the Pearson's correlation coefficient (R) was 0.91 (P < 0.001) for both observers. The inter-observer study resulted in a Dice similarity index of 89.0 ± 2.4% for segmenting the pericardium and a Pearson's correlation coefficient of 0.92 (P<0.001) for computation of the epicardial fat volume.
The authors developed a fully automatic method that is capable of segmenting the pericardium and quantifying epicardial fat on non-enhanced cardiac CT scans. The authors demonstrated the feasibility of using this method to replace manual annotations by showing that the automatic method performs as good as manual annotation on a large dataset.
越来越多的证据表明，心外膜脂肪（即心包内的脂肪组织）在心血管疾病的发展中起着重要作用。因此，从常规进行的非增强心脏 CT 扫描中获得心外膜脂肪量具有临床意义。本研究旨在探讨在非增强心脏 CT 扫描上自动分割心包并随后定量心外膜脂肪的可行性。
检索了来自在我们医疗中心进行心脏 CT 扫描的较大队列中随机选择的 98 名受试者的成像数据。这些数据是在两台不同的扫描仪上采集的。已开发出一种用于分割心包和计算心外膜脂肪量的自动多图谱方法。通过（1）将自动分割的心包与手动标注的参考标准进行比较，（2）将自动获得的心外膜脂肪量与手动获得的心外膜脂肪量进行比较，以及（3）将自动结果的准确性与观察者间的变异性进行比较，评估该方法的性能。
自动分割心包的 Dice 相似性指数分别为观察者 1 的 89.1 ± 2.6%和观察者 2 的 89.2 ± 1.9%。对于自动方法与手动观察者计算的作为 Pearson 相关系数（R）的心外膜脂肪量之间的相关性，对于两位观察者均为 0.91（P<0.001）。观察者间研究得出的分割心包的 Dice 相似性指数为 89.0 ± 2.4%，计算心外膜脂肪量的 Pearson 相关系数为 0.92（P<0.001）。
作者开发了一种全自动方法，能够在非增强心脏 CT 扫描上分割心包并定量心外膜脂肪。作者通过证明自动方法在大型数据集上的表现与手动标注一样好，证明了使用这种方法替代手动标注的可行性。
