Automated 3-dimensional MRI segmentation for the posterosuperior rotator cuff tear lesion using deep learning algorithm.

Journal: PloS one
Published Date: May 18, 2023

Abstract

INTRODUCTION: Rotator cuff tear (RCT) is a challenging and common musculoskeletal disease. Magnetic resonance imaging (MRI) is a commonly used diagnostic modality for RCT, but the interpretation of the results is tedious and has some reliability issues. In this study, we aimed to evaluate the accuracy and efficacy of the 3-dimensional (3D) MRI segmentation for RCT using a deep learning algorithm.

Authors

  • Su Hyun Lee
    Department of Radiology, Seoul National University Hospital and Seoul National University College of Medicine, Seoul 110-744, South Korea.
  • Jihwan Lee
    Department of Industrial and Data Engineering, Major in Industrial Data Science and Engineering, Pukyong National University, Busan 48513, Korea.
  • Kyung-Soo Oh
    a Department of Orthopaedic Surgery and.
  • Jong Pil Yoon
    d Department of Orthopaedic Surgery , Kyungpook National University College of Medicine , Daegu , Korea.
  • Anna Seo
    SEEANN Solution, Yeonsu-gu, Incheon, Korea.
  • YoungJin Jeong
    SEEANN Solution, Yeonsu-gu, Incheon, Korea.
  • Seok Won Chung
    a Department of Orthopaedic Surgery and.

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