A Cascaded Deep Learning Framework for Detecting Aortic Dissection Using Non-contrast Enhanced Computed Tomography
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)(2021)
摘要
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.
更多查看译文
关键词
detecting aortic dissection,aortic dissection,cascaded deep learning framework,deep learning,tomography,non-contrast
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要