Aarhus University Seal

Jan Wolff

A review on the application of deep learning for CT reconstruction, bone segmentation and surgical planning in oral and maxillofacial surgery

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperReviewResearchpeer-review


  • Jordi Minnema, Vrije Universiteit Amsterdam, Netherlands
  • Anne Ernst, University of Hamburg, Germany
  • Maureen van Eijnatten, Computational imaging group, Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands, Eindhoven University of Technology, Netherlands
  • Ruben Pauwels
  • Tymour Forouzanfar, Vrije Universiteit Amsterdam, Netherlands
  • Kees Joost Batenburg, Computational imaging group, Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands, Leiden University, Netherlands
  • Jan Wolff

Computer-assisted surgery (CAS) allows clinicians to personalize treatments and surgical interventions and has therefore become an increasingly popular treatment modality in maxillofacial surgery. The current maxillofacial CAS consists of three main steps: (1) CT image reconstruction, (2) bone segmentation, and (3) surgical planning. However, each of these three steps can introduce errors that can heavily affect the treatment outcome. As a consequence, tedious and time-consuming manual post-processing is often necessary to ensure that each step is performed adequately. One way to overcome this issue is by developing and implementing neural networks (NNs) within the maxillofacial CAS workflow. These learning algorithms can be trained to perform specific tasks without the need for explicitly defined rules. In recent years, an extremely large number of novel NN approaches have been proposed for a wide variety of applications, which makes it a difficult task to keep up with all relevant developments. This study therefore aimed to summarize and review all relevant NN approaches applied for CT image reconstruction, bone segmentation, and surgical planning. After full text screening, 76 publications were identified: 32 focusing on CT image reconstruction, 33 focusing on bone segmentation and 11 focusing on surgical planning. Generally, convolutional NNs were most widely used in the identified studies, although the multilayer perceptron was most commonly applied in surgical planning tasks. Moreover, the drawbacks of current approaches and promising research avenues are discussed.

Original languageEnglish
Article number20210437
JournalDento maxillo facial radiology
Pages (from-to)20210437
Publication statusPublished - Sept 2022

    Research areas

  • Deep Learning, Humans, Image Processing, Computer-Assisted/methods, Neural Networks, Computer, Surgery, Oral, Tomography, X-Ray Computed/methods

See relations at Aarhus University Citationformats

ID: 270086944