Open Collections

Yang, Rui

University of British Columbia

Mandibular meshes segmented from computerized tomography (CT) images contain rich information of the dentition. Inconsistent dentition conditions in healthy mandible data sets can impair data-driven premorbid shape prediction for diseased mandibles. We developed a mesh segmentation method that includes a preprocessing algorithm using an off-the-shelf non-rigid registration, a surface mesh feature function, and an active contour model using geodesic distance. Constructive Solid Geometry (CSG) operations are employed to separate the dentition area from the mandibular mesh. An easy-to-use interactive tool was implemented, allowing users to adjust the contour position. We evaluated our method (preprocessing algorithm and user intervention) by comparing it with the traditional method of manual removal using 3D Slicer. The results indicated that our method helped save the manual processing time by 40%, which largely improves the efficiency. From a statistical-shape-modeling-based shape completion test, we drew the conclusion that edentulous mandibular data set could help make significantly better premorbid shape predictions (Z=-2.484,p=0.013) than data set with mixed dentition conditions. Besides tooth segmentation from 3D meshes, our research can assist virtual planning for bone graft placement in the defective mandible and implant placement in the bone graft. This work forms the underlying basis of a useful tool for coupling jaw reconstruction and restorative dentition for patient treatment planning.

Text

2021-08-27

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/79477

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/