Brain CT registration using hybrid supervised convolutional neural network.

Journal: Biomedical engineering online
Published Date: Dec 29, 2021

Abstract

BACKGROUND: Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly remains greatly challenging due to the large intersubject anatomical variations, low resolution of soft tissues, and heavy computation costs. To this end, the HSCN-Net, a hybrid supervised convolutional neural network, was developed for precise and fast brain CT registration.

Authors

  • Hongmei Yuan
    Neusoft Research of Intelligent Healthcare Technology, Co. Ltd, A1 Building, No.2 Xinxiu Street, Hunnan New District, Shenyang, 110179, People's Republic of China.
  • Minglei Yang
    Biomedical Engineering, CT Collaboration of Siemens Healthineers, No. 278, Zhouzhu Road, Pudong New District, Shanghai, 201318, People's Republic of China.
  • Shan Qian
    Neusoft Research of Intelligent Healthcare Technology, Co. Ltd, A1 Building, No.2 Xinxiu Street, Hunnan New District, Shenyang, 110179, People's Republic of China.
  • Wenxin Wang
    College of Life Science and Bio-Engineering, Beijing University of Technology, No. 100 Pingleyuan, Chaoyang District, Beijing 100124, China; Neusoft Medical System, Neusoft Beijing R&D Center, Zhongguancun Software Park 10, Xibeiwang East Road, Haidian District, Beijing 100194, China.
  • Xiaotian Jia
    Shenyang Advanced Medical Equipment Technology Incubation Center, Co. Ltd, Shenyang, 110167, China.
  • Feng Huang
    Beijing Hospital of TCM, Capital Medical University, Beijing 100010, China; Institution of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing 100700.

Keywords

External Resources

Popular Topics
Recent Journals