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

Abstract

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.

A Deep Learning Approach For Dental Implant Planning On Cone-Beam Computed Tomography Images

“…al have used the CNN method for mandibular canal segmentation in al CBCT images. They stated that AI systems give sensitive and reliable results in canal determination and these systems may be an important role in future implant planning [44]. The results of our study were similar to these studies; and its success percentage was 97.9% in the mandibular canal detection.…”

A deep learning approach for dental implant planning in cone-beam computed tomography images

“…al have used the CNN method for mandibular canal segmentation in al CBCT images. They stated that AI systems give sensitive and reliable results in canal determination and these systems may be an important role in future implant planning [44].…”

Deep learning based prediction of extraction difficulty for mandibular third molars

“…CNNs have shown excellent results in the analysis of radiographic images when compared to the results by medical experts. Previous studies have shown that deep learning can be used to recognize anatomical structures, find anomalies, measure the distance, and classify structures in medical images 1,[3][4][5][6][7][8][9][10][11][12][13][14][15] . However, in most studies, object detection was conducted manually, and tasks were limited to performing simple measurements, comparisons, or classifications.…”

“…In recent years, CNNs have been extensively used in many fields. In the healthcare industry, numerous studies have reported that a CNN can be used to analyze and diagnose medical images [1][2][3][4][5][6][7] . CNNs have also been used to better interpret the complexities of medical imaging by revealing patterns in large numbers of data and acquiring essential information to gain more knowledge 2 .…”

“…In the field of dentistry, CNNs have been applied for the detection of carious lesions, periodontal lesions, mandibular canals, cysts, and tumors 1,3,4,[7][8][9] . They are also used to assess the difficulty of realizing endodontic treatment, skeletal classification, soft tissue profiling, osteoporosis, root morphology evaluation, and survival prediction of oral cancer patients 5,[10][11][12][13][14] .…”