A Cascaded Deep Learning Framework for Detecting Aortic Dissection Using Non-contrast Enhanced Computed Tomography.

Journal: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Published Date: Nov 1, 2021

Abstract

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.

Authors

  • Xiangyu Xiong
  • Xiuhong Guan
  • Chuanqi Sun
  • Tianjing Zhang
    Philips Healthcare, Guangzhou, China.
  • Hao Chen
    The First School of Medicine, Wenzhou Medical University, Wenzhou, China.
  • Yan Ding
    Department of Computer Science and Engineering, Changshu Institute of Technology, Changshu 215500, China.
  • Zhangbo Cheng
  • Lei Zhao
    Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China.
  • Xiaohai Ma
    Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Guoxi Xie
    School of Basic Medical Sciences, Guangzhou Medical University, Guangzhou, China, 510182.

Keywords

External Resources

Popular Topics
Recent Journals