Snake-based interactive tooth segmentation for 3D mandibular meshes

Rui Yang; Amir H. Abdi; Atabak Eghbal; Edward Wang; Khanh Linh Tran; David Yang; Antony Hodgson; Eitan Prisman; Sidney Fels

doi:10.1117/12.2581988

15 February 2021 Snake-based interactive tooth segmentation for 3D mandibular meshes

Rui Yang, Amir H. Abdi, Atabak Eghbal, Edward Wang, Khanh Linh Tran, David Yang, Antony Hodgson, Eitan Prisman, Sidney Fels

Author Affiliations +

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 115981O (2021) https://doi.org/10.1117/12.2581988
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

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. 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. An easy-to-use interactive tool is developed, allowing users to adjust the tooth crown contour position. A validation study and a comparison study were conducted for method evaluation. In the validation study, we removed teeth for 28 models acquired from Vancouver General Hospital (VGH) and ran a shape completion test. 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). The volumetric Dice score (DSC) shows no significant difference. 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 result indicates that our method reduced the manual processing time by 40% on average and approached the accuracy of manual tooth segmentation. It is promising to warrant further efforts towards clinical usage. This work forms the basis of a useful tool for coupling jaw reconstruction and restorative dentition for patient treatment planning.

Conference Presentation

Citation Download Citation

Rui Yang, Amir H. Abdi, Atabak Eghbal, Edward Wang, Khanh Linh Tran, David Yang, Antony Hodgson, Eitan Prisman, and Sidney Fels "Snake-based interactive tooth segmentation for 3D mandibular meshes", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115981O (15 February 2021); https://doi.org/10.1117/12.2581988

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