Mandible segmentation from CT data for virtual surgical planning using an augmented two-stepped convolutional neural network.

Journal: International journal of computer assisted radiology and surgery
Published Date: Jan 13, 2023

Abstract

PURPOSE: For computer-aided planning of facial bony surgery, the creation of high-resolution 3D-models of the bones by segmenting volume imaging data is a labor-intensive step, especially as metal dental inlays or implants cause severe artifacts that reduce the quality of the computer-tomographic imaging data. This study provides a method to segment accurate, artifact-free 3D surface models of mandibles from CT data using convolutional neural networks.

Authors

  • Tobias Pankert
    Department of Oral and Maxillofacial Surgery, RWTH Aachen University Hospital, Aachen, Germany. tpankert@ukaachen.de.
  • Hyun Lee
    Department of Internal Medicine, Hanyang University Hospital, Hanyang University College of Medicine, 222 Wangsimri-ro, Seongdong-gu, Seoul 04763, Korea.
  • Florian Peters
    Department of Oral and Maxillofacial Surgery, RWTH Aachen University Hospital, Aachen, Germany.
  • Frank Hölzle
    Department of Oral and Maxillofacial Surgery, RWTH Aachen University Hospital, Aachen, Germany.
  • Ali Modabber
    Department of Oral and Maxillofacial Surgery, RWTH Aachen University Hospital, Aachen, Germany.
  • Stefan Raith
    Department of Dental Materials and Biomaterials Research, RWTH Aachen University Hospital, Aachen, Germany. Electronic address: sraith@ukaachen.de.

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