Automated 3D segmentation of the hyoid bone in CBCT using nnU-Net v2: a retrospective study on model performance and potential clinical utility.

Journal: BMC medical imaging
Published Date: Jul 1, 2025

Abstract

OBJECTIVE: This study aimed to identify the hyoid bone (HB) using the nnU-Net based artificial intelligence (AI) model in cone beam computed tomography (CBCT) images and assess the model's success in automatic segmentation.

Authors

  • Ismail Gumussoy
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Sakarya University, Mithatpaşa Mah. Adnan Menderes Cad. No:122/B, Adapazarı, Sakarya, 54100, Turkey.
  • Emre Haylaz
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Sakarya University, Mithatpaşa Mah. Adnan Menderes Cad. No:122/B, Adapazarı, Sakarya, 54100, Turkey. emrehylz03@gmail.com.
  • Şuayip Burak Duman
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University Malatya, Malatya, Turkey. suayipburakduman@gmail.com.
  • Fahrettin Kalabalik
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Sakarya University, Adapazarı, Sakarya, 54100, Turkey.
  • Seyda Say
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Sakarya University, Adapazarı, Sakarya, 54100, Turkey.
  • Özer Çelik
    Department of Mathematics and Computer, Faculty of Science and Letters, Eskişehir Osmangazi University, Eskişehir, Turkey.
  • İbrahim Şevki Bayrakdar
    Department of Oral Diagnosis and Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir, Turkey.

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