SSAT-Swin: Deep Learning-Based Spinal Ultrasound Feature Segmentation for Scoliosis Using Self-Supervised Swin Transformer.

Journal: Ultrasound in medicine & biology
PMID: 40082183

Abstract

OBJECTIVE: Scoliosis, a 3-D spinal deformity, requires early detection and intervention. Ultrasound curve angle (UCA) measurement using ultrasound images has emerged as a promising diagnostic tool. However, calculating the UCA directly from ultrasound images remains challenging due to low contrast, high noise, and irregular target shapes. Accurate segmentation results are therefore crucial to enhance image clarity and precision prior to UCA calculation.

Authors

  • Chen Zhang
    Department of Dermatology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
  • Yongping Zheng
    Department of Biomedical Engineering, Interdisciplinary Division of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
  • Jeb McAviney
    ScoliCare Clinic Sydney (South), Kogarah, NSW, Australia.
  • Sai Ho Ling
    Centre for Health Technologies, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW, Australia. Electronic address: Steve.Ling@uts.edu.au.

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