Abstract：
Thoracic aorta calcium (TAC) can be assessed from cardiac computed tomography (CT) studies to improve cardiovascular risk prediction. The aim of this study was to develop a fully automatic system to detect TAC and to evaluate its performance for classifying the patients into four TAC risk categories. The method started by segmenting the thoracic aorta, combining three UNets trained with axial, sagittal and coronal CT images. Afterwards, the surrounding lesion candidates were classified using three combined convolutional neural networks (CNNs) trained with orthogonal patches. Image datasets included 1190 non-enhanced ECG-gated cardiac CT studies from a cohort of cardiovascular patients (age 57 ± 9 years, 80% men, 65% TAC > 0). In the test set (N = 119), the combination of UNets was able to successfully segment the thoracic aorta with a mean volume difference of 0.3 ± 11.7 ml (<6%) and a median Dice coefficient of 0.947. The combined CNNs accurately classified the lesion candidates and 87% of the patients (N = 104) were accurately placed in their corresponding risk categories (Kappa = 0.826, ICC = 0.9915). TAC measurement can be estimated automatically from cardiac CT images using UNets to isolate the thoracic aorta and CNNs to classify calcified lesions.
摘要：
可以通过心脏计算机断层扫描（CT）研究评估胸主动脉钙（TAC），以改善心血管风险预测。这项研究的目的是开发一种全自动系统来检测TAC并评估其将患者分为四个TAC风险类别的性能。该方法从分割胸主动脉开始，结合三个受过轴向训练的UNets，矢状和冠状CT图像。之后,周围候选病变使用三个用正交补片训练的组合卷积神经网络(CNN)进行分类.图像数据集包括来自一组心血管患者（年龄57±9岁，80%的男性,65%TAC>0)。在测试集（N=119）中，UNets组合能够成功分割胸主动脉,平均容积差为0.3±11.7ml(<6%),中位Dice系数为0.947.合并的CNN对候选病变进行了准确分类，87％的患者（N=104）被准确地置于其相应的风险类别中（Kappa=0.826，ICC=0.9915）。可以使用UNet从心脏CT图像自动估计TAC测量以隔离胸主动脉和CNN以对钙化病变进行分类。
.
