Alveolar Bone and Mandibular Canal Segmentation on Cone Beam Computed Tomography Images Using U-Net

Monica Widiasri*, Nanik Suciati, Chastine Fatichah, Eha Renwi Astuti, Ramadhan Hardani Putra, Agus Zainal Arifin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

A dental implant is a treatment to replace missing teeth. Determining the proper dimensions of dental implants is measured by observing the distance from the mandibular canal (MC) to the alveolar bone (AB). It is crucial to pay careful attention to the location of the MC when planning for a dental implant in the posterior mandible to avoid injury. Therefore, segmenting AB and MC in dental implant planning is essential. While research on MC segmentation using deep learning has been conducted extensively, there has yet to be much research on AB and MC segmentation simultaneously with deep learning. This study proposes using U-Net as a high-performance segmentation technique for multiclass and binary segmentation to segment AB and MC regions. In the output branch of the U-Net architecture, two scenarios are designed, the first is to perform AB and MC segmentation simultaneously, while the second is to perform AB and MC segmentation separately. The study used 563 2D grayscale Cone Beam Computed Tomography (CBCT) images from the coronal slice. The model is trained and tested using K-fold cross validation. The test results show that AB and MC segmentation simultaneously produces the mean intersection of union (IoU) value of 0.85. Meanwhile, AB and MC segmentation separately produced the mean IoU of 0.98 for AB segmentation and 0.81 for MC segmentation. The results of the satisfactory AB and MC segmentation are expected to assist determine implant dimensions in dental implant planning.

Original languageEnglish
Title of host publicationProceedings of the 2023 International Conference on Instrumentation, Control, and Automation, ICA 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages36-41
Number of pages6
ISBN (Electronic)9798350301274
DOIs
Publication statusPublished - 2023
Event8th International Conference on Instrumentation, Control, and Automation, ICA 2023 - Jakarta, Indonesia
Duration: 9 Aug 202311 Aug 2023

Publication series

NameProceedings of the 2023 International Conference on Instrumentation, Control, and Automation, ICA 2023

Conference

Conference8th International Conference on Instrumentation, Control, and Automation, ICA 2023
Country/TerritoryIndonesia
CityJakarta
Period9/08/2311/08/23

Keywords

  • K-fold cross validation
  • U-Net
  • alveolar bone
  • deep learning
  • dental implants
  • mandibular canal
  • segmentation

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