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The goal of this study is to establish a neural network that can create automatic mandible segmentations based on head cbct scans. Notably, we utilized a demographically diverse dataset of 648 manually segmented cbct images which also included a high degree of metal artifacts. Segmented mandible structures are used to effectively visualize the mandible volumes and to evaluate particular mandible properties quantitatively.
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A dataset of 90 cbct scans was annotated as ground truth for mandibular canal segmentation. In this paper, we trained a convolutional neural network (cnn) to produce automatic segmentations of the mandible and lower dentition from cbct scans The application of deep learning in developing automated segmentation models offers the potential for substantial reductions in the time required for manual segmentation.
