Their result was also affected by the various dentition conditions in their samples. created statistical shape models (SSM) to determine the premorbid shapes for diseased mandibles 2. A major limitation was that the inconsistency in the presence of teeth across the training samples caused errors regarding bone-loss 1. extended a conditional variational autoencoder to generate missing shapes for defective mandibles. A variety of computer-aided design and manufacturing tools have been developed based on 3D meshes. Over the last decade, oral reconstructive surgeries have benefited substantially from virtual surgical planning (VSP).
This work forms the basis of a useful tool for coupling jaw reconstruction and restorative dentition for patient treatment planning. It is promising to warrant further efforts towards clinical usage. The result indicates that our method reduced the manual processing time by 40% on average and approached the accuracy of manual tooth segmentation. In the second study, we compared the proposed method to manual removal in terms of manual processing time, symmetric HD95, and symmetric root mean square deviation (RMSD). The volumetric Dice score (DSC) shows no significant difference. Regarding 95th percentile Hausdorff distance (HD95), using edentulous models produced significantly better predictions of the premorbid shapes of diseased mandibles than using models with inconsistent dentition conditions(Z = -2.484, p = 0.01).
In the validation study, we removed teeth for 28 models acquired from Vancouver General Hospital (VGH) and ran a shape completion test. A validation study and a comparison study were conducted for method evaluation. An easy-to-use interactive tool is developed, allowing users to adjust the tooth crown contour position. To locate the alveolar process and remove the dentition area, we propose a tooth segmentation method including a preprocessing step using non-rigid registration, an active contour model, and constructive solid geometry (CSG) operations. Mandibular meshes segmented from computerized tomography (CT) images contain rich information of the dentition conditions, which impairs the performance of shape completion algorithms relying on such data, but can benefit virtual planning for oral reconstructive surgeries.
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