A Cascaded Deep Learning Framework for Detecting Aortic Dissection Using Non-contrast Enhanced Computed Tomography
Xiangyu Xiong, Xiuhong Guan, Chuanqi Sun, Tianjing Zhang, Hao Chen, Yan Ding, Zhangbo Cheng, Lei Zhao, Xiaohai Ma, Guoxi Xie · 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) · 2021
Aortic dissection (AD) is a rare but potentially fatal disease with high mortality. The aim of this study is to synthesize contrast enhanced computed tomography (CE-CT) images from non-contrast CT (NCE-CT) images for detecting aortic dissection. In this paper, a cascaded deep learning framework containing a 3D segmentation network and a synthetic network was proposed and evaluated. A 3D segmentation network was firstly used to segment aorta from NCE-CT images and CE-CT images. A conditional generative adversarial network (CGAN) was subsequently employed to map the NCE-CT images to the CE-CT images non-linearly for the region of aorta. The results of the experiment suggest that the cascaded deep learning framework can be used for detecting the AD and outperforms CGAN alone.