A Deep Learning Pipeline Using Prior Knowledge for Automatic Evaluation of Placenta Accreta Spectrum Disorders With MRI.

Abstract

Authors

• Haijie Wang
  Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China.
• Yida Wang
  Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China. Electronic address: ydwang@phy.ecnu.edu.cn.
• He Zhang
  College of Natural Resources and Environment, Northwest A&F University, Yangling, 712100, Shaanxi, PR China; Key Laboratory of Plant Nutrition and the Agri-environment in Northwest China, Ministry of Agriculture and Rural Affairs, Yangling, 712100, Shaanxi, PR China.
• Xuan Yin
  Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
• Chenglong Wang
  Plastic Surgery Hospital, Chinese Academy of Medical Science, Peking Union Medical College, Beijing, China.
• Yuanyuan Lu
  Department of Radiology, Shanghai First Maternity and Infant Health Hospital, School of Medicine, Tongji University, Shanghai, China.
• Yang Song
  Biomedical and Multimedia Information Technology (BMIT) Research Group, School of IT, University of Sydney, NSW 2006, Australia. Electronic address: yson1723@uni.sydney.edu.au.
• Hao Zhu
  State Key Laboratory of Advanced Technology for Materials Synthesis and Processing, Wuhan University of Technology Wuhan 430070 PR China chang@whut.edu.cn suntl@whut.edu.cn.
• Guang Yang
  National Heart and Lung Institute, Imperial College London, London, UK.

Keywords

External Resources