Active shape models—their training and application
Computer Vision and Image Understanding
TEASAR: Tree-Structure Extraction Algorithm for Accurate and Robust Skeletons
PG '00 Proceedings of the 8th Pacific Conference on Computer Graphics and Applications
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Automatic segmentation of the pelvic bones from CT data based on a statistical shape model
EG VCBM'08 Proceedings of the First Eurographics conference on Visual Computing for Biomedicine
Improving deformable surface meshes through omni- directional displacements and MRFs
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Computer Methods and Programs in Biomedicine
Automatic detection and classification of teeth in CT data
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
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The exact localization of the mandibular nerve with respect to the bone is important for applications in dental implantology and maxillofacial surgery. Cone beam computed tomography (CBCT), often also called digital volume tomography (DVT), is increasingly utilized in maxillofacial or dental imaging. Compared to conventional CT, however, soft tissue discrimination is worse due to a reduced dose. Thus, small structures like the alveolar nerves are even harder recognizable within the image data. We show that it is nonetheless possible to accurately reconstruct the 3D bone surface and the course of the nerve in a fully automatic fashion, with a method that is based on a combined statistical shape model of the nerve and the bone and a Dijkstra-based optimization procedure. Our method has been validated on 106 clinical datasets: the average reconstruction error for the bone is 0.5±0.1 mm, and the nerve can be detected with an average error of 1.0±0.6 mm.