Improving myocardial pathology segmentation with U-Net++ and EfficientSeg from multi-sequence cardiac magnetic resonance images.

Journal: Computers in biology and medicine
PMID: 36308898

Abstract

BACKGROUND: Myocardial pathology segmentation plays an utmost role in the diagnosis and treatment of myocardial infarction (MI). However, manual segmentation is time-consuming and labor-intensive, and requires a lot of professional knowledge and clinical experience.

Authors

  • Hengfei Cui
    National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science, Northwestern Polytechnical University, China.
  • Yan Li
    Interdisciplinary Research Center for Biology and Chemistry, Liaoning Normal University, Dalian, China.
  • Lei Jiang
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China.
  • Yifan Wang
    School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China.
  • Yong Xia
  • Yanning Zhang

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