Automated segmentation and deep learning classification of ductopenic parotid salivary glands in sialo cone-beam CT images.

Journal: International journal of computer assisted radiology and surgery
PMID: 39085681

Abstract

PURPOSE: This study addressed the challenge of detecting and classifying the severity of ductopenia in parotid glands, a structural abnormality characterized by a reduced number of salivary ducts, previously shown to be associated with salivary gland impairment. The aim of the study was to develop an automatic algorithm designed to improve diagnostic accuracy and efficiency in analyzing ductopenic parotid glands using sialo cone-beam CT (sialo-CBCT) images.

Authors

  • Elia Halle
    Department of Data Mining, Jerusalem College of Technology, Jerusalem, Israel.
  • Tevel Amiel
    Oral Maxillofacial Imaging Unit, Department of Oral Medicine, Sedation and Imaging, Faculty of Dental Medicine, The Hebrew University of Jerusalem, Hadassah Medical Center, Jerusalem, Israel.
  • Doron J Aframian
    Department of Oral Medicine, Sedation and Imaging, Faculty of Dental Medicine, The Hebrew University of Jerusalem, Hadassah Medical Center, Jerusalem, Israel.
  • Tal Malik
    Department of Data Mining, Jerusalem College of Technology, Jerusalem, Israel.
  • Avital Rozenthal
    Department of Data Mining, Jerusalem College of Technology, Jerusalem, Israel.
  • Oren Shauly
    School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
  • Leo Joskowicz
    School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
  • Chen Nadler
    Lecturer, Oral Maxillofacial Imaging Unit, Oral Medicine Department, the Hebrew University, Hadassah School of Dental Medicine, Ein Kerem, Hadassah Medical Center Jerusalem, Israel. Electronic address: Nadler@hadassah.org.il.
  • Talia Yeshua
    Lecturer, Department of Applied Physics/Electro-optics Engineering, The Jerusalem College of Technology, Jerusalem, Israel.

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