Deep Learning Method for Mandibular Canal Segmentation in Dental Cone Beam Computed Tomography Volumes

Joel Jaskari, Jaakko Sahlsten, Jorma Järnstedt, Helena Mehtonen, Kalle Karhu, Osku Sundqvist, Ari Hietanen, Vesa Varjonen, Vesa Mattila, Kimmo Kaski

Research output: Contribution to journalArticleScientificpeer-review

63 Citations (Scopus)
109 Downloads (Pure)


Accurate localisation of mandibular canals in lower jaws is important in dental implantology, in which the implant position and dimensions are currently determined manually from 3D CT images by medical experts to avoid damaging the mandibular nerve inside the canal. Here we present a deep learning system for automatic localisation of the mandibular canals by applying a fully convolutional neural network segmentation on clinically diverse dataset of 637 cone beam CT volumes, with mandibular canals being coarsely annotated by radiologists, and using a dataset of 15 volumes with accurate voxel-level mandibular canal annotations for model evaluation. We show that our deep learning model, trained on the coarsely annotated volumes, localises mandibular canals of the voxel-level annotated set, highly accurately with the mean curve distance and average symmetric surface distance being 0.56 mm and 0.45 mm, respectively. These unparalleled accurate results highlight that deep learning integrated into dental implantology workflow could significantly reduce manual labour in mandibular canal annotations.

Original languageEnglish
Article number5842
Pages (from-to)1-8
Number of pages8
JournalScientific Reports
Issue number1
Publication statusPublished - 1 Dec 2020
MoE publication typeA1 Journal article-refereed


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