A dual-decoder banded convolutional attention network for bone segmentation in ultrasound images.

Journal: Medical physics
PMID: 39651711

Abstract

BACKGROUND: Ultrasound (US) has great potential for application in computer-assisted orthopedic surgery (CAOS) due to its non-radiative, cost-effective, and portable traits. However, bone segmentation from low-quality US images has been challenging. Traditional segmentation methods cannot achieve satisfactory results due to their high customization and dependence on bone morphology. Existing deep learning-based methods make it difficult to ensure efficient and accurate segmentation due to the ignorance of prior knowledge of bone features during feature learning.

Authors

  • Chuanba Liu
    Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China.
  • Wenshuo Wang
    Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China.
  • Rui Sun
    The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou 325027, China.
  • Teng Wang
    Department of Nutrition and food hygiene, College of Public Health of Zhengzhou University, Zhengzhou, China, 450001. Electronic address: 530327182@qq.com.
  • Xiantao Shen
    Department of Orthopaedics, Wuhan Children's Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Tao Sun
    Janssen Research & Development, LLC, Raritan, NJ, USA.

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