Automatic segmentation of jaw tissues in CT using active appearance models and semi-automatic landmarking

Authors:
Sylvia Rueda;José Antonio Gil;Raphaël Pichery;Mariano Alcañiz
Affiliations:
Medical Image Computing Laboratory (MedICLab), Universidad Politécnica de Valencia, UPV/ETSIA, Valencia, Spain;Medical Image Computing Laboratory (MedICLab), Universidad Politécnica de Valencia, UPV/ETSIA, Valencia, Spain;Ecole Supérieure d’Ingénieurs de Luminy, Marseille, France;Medical Image Computing Laboratory (MedICLab), Universidad Politécnica de Valencia, UPV/ETSIA, Valencia, Spain
Venue:
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2006

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Abstract

Preoperative planning systems are commonly used for oral implant surgery. One of the objectives is to determine if the quantity and quality of bone is sufficient to sustain an implant while avoiding critical anatomic structures. We aim to automate the segmentation of jaw tissues on CT images: cortical bone, trabecular core and especially the mandibular canal containing the dental nerve. This nerve must be avoided during implant surgery to prevent lip numbness. Previous work in this field used thresholds or filters and needed manual initialization. An automated system based on the use of Active Appearance Models (AAMs) is proposed. Our contribution is a completely automated segmentation of tissues and a semi-automatic landmarking process necessary to create the AAM model. The AAM is trained using 215 images and tested with a leave-4-out scheme. Results obtained show an initialization error of 3.25% and a mean error of 1.63mm for the cortical bone, 2.90mm for the trabecular core, 4.76mm for the mandibular canal and 3.40mm for the dental nerve.