Automatic segmentation of the maxillary sinus on cone beam computed tomographic images with U-Net deep learning model.

Journal: European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PMID: 39083060

Abstract

BACKGROUND: Medical imaging segmentation is the use of image processing techniques to expand specific structures or areas in medical images. This technique is used to separate and display different textures or shapes in an image. The aim of this study is to develop a deep learning-based method to perform maxillary sinus segmentation using cone beam computed tomography (CBCT) images. The proposed segmentation method aims to provide better image guidance to surgeons and specialists by determining the boundaries of the maxillary sinus cavities. In this way, more accurate diagnoses can be made and surgical interventions can be performed more successfully.

Authors

  • Busra Ozturk
    Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Meram, Konya, 42050, Turkey.
  • Yavuz Selim Taspinar
    Doganhisar Vocational School, Selcuk University, Konya, Turkey.
  • Murat Koklu
    Department of Computer Engineering, Faculty of Technology, Selcuk University, Konya, Turkey. Electronic address: mkoklu@selcuk.edu.tr.
  • Melek Tassoker
    Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Baglarbasi sk, Meram, Konya, 42050, Türkiye. dishekmelek@gmail.com.

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