A Shape-Consistent Deep-Learning Segmentation Architecture for Low-Quality and High-Interference Myocardial Contrast Echocardiography.

Journal: Ultrasound in medicine & biology
PMID: 39147622

Abstract

OBJECTIVE: Myocardial contrast echocardiography (MCE) plays a crucial role in diagnosing ischemia, infarction, masses and other cardiac conditions. In the realm of MCE image analysis, accurate and consistent myocardial segmentation results are essential for enabling automated analysis of various heart diseases. However, current manual diagnostic methods in MCE suffer from poor repeatability and limited clinical applicability. MCE images often exhibit low quality and high noise due to the instability of ultrasound signals, while interference structures can further disrupt segmentation consistency.

Authors

  • Rongpu Cui
    College of Computer Science, Sichuan University, Chengdu, China.
  • Shichu Liang
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
  • Weixin Zhao
    College of Computer Science, Sichuan University, Chengdu, China.
  • Zhiyue Liu
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
  • Zhicheng Lin
    Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom.
  • Wenfeng He
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
  • Yujun He
    College of Computer Science, Sichuan University, Chengdu, China.
  • Chaohui Du
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu, China.
  • Jian Peng
    Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL, USA.
  • He Huang

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