论文标题
基于深度学习的检测急性主动脉综合征在对比CT图像中
Deep Learning based detection of Acute Aortic Syndrome in contrast CT images
论文作者
论文摘要
急性主动脉综合征（AAS）是主动脉的一系列威胁生命的疾病。我们已经开发了一种端到端自动方法来检测计算机断层扫描（CT）图像中的AA。我们的方法包括两个步骤。首先，我们沿每个CT扫描的分段主动脉中心线提取N横截面。将这些横截面堆叠在一起以形成一个新的卷，然后使用两个不同的分类器分类，即3D卷积神经网络（3D CNN）和多个实例学习（MIL）。我们在2291个对比度CT卷上训练，验证并比较了两种模型。我们在230个正常和50个正CT体积的旁边进行了测试。我们的模型分别使用3DCNN和MIL检测到接收器操作特性曲线（AUC）的AAS，分别为0.965和0.985。
Acute aortic syndrome (AAS) is a group of life threatening conditions of the aorta. We have developed an end-to-end automatic approach to detect AAS in computed tomography (CT) images. Our approach consists of two steps. At first, we extract N cross sections along the segmented aorta centerline for each CT scan. These cross sections are stacked together to form a new volume which is then classified using two different classifiers, a 3D convolutional neural network (3D CNN) and a multiple instance learning (MIL). We trained, validated, and compared two models on 2291 contrast CT volumes. We tested on a set aside cohort of 230 normal and 50 positive CT volumes. Our models detected AAS with an Area under Receiver Operating Characteristic curve (AUC) of 0.965 and 0.985 using 3DCNN and MIL, respectively.
