Mandible Segmentation from CT and CBCT Images Based on a Patch-Based Convolutional Neural Network

Mandible segmentation from computed tomography (CT) and cone beam computed tomography (CBCT) images has several applications in craniomaxillofacial and oral surgery planning, as well as in implant dentistry. These applications include planning using virtual models, such as measurements and implant positioning, as well as physical models, such as the molding of plates for fracture correction on 3D-printed models before surgery. Traditional image segmentation algorithms do not yield satisfactory results because it is common for the mandibular teeth to be occluded with the maxillary teeth. Another region that presents the same problem is the condyle head with the temporal bone. In light of this, this paper investigates the problem of mandible segmentation from CT and CBCT images using a patch-based convolutional neural network. We also describe the creation of ground truth for our dataset from a triangular mesh.