Rueda Sylvia, Gil José Antonio, Pichery Raphaël, Alcañiz Mariano
Medical Image Computing Laboratory, Universidad Politécnica de Valencia, UPV/ETSIA, Camino de Vera s/n, 46022 Valencia, Spain.
Med Image Comput Comput Assist Interv. 2006;9(Pt 1):167-74. doi: 10.1007/11866565_21.
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, 2.90 mm for the trabecular core, 4.76 mm for the mandibular canal and 3.40 mm for the dental nerve.
术前规划系统常用于口腔种植手术。其目标之一是确定骨量和骨质量是否足以支撑种植体，同时避开关键解剖结构。我们旨在实现CT图像上颌骨组织的自动分割，包括皮质骨、小梁核心，尤其是包含牙神经的下颌管。在种植手术过程中必须避开此神经以防止唇部麻木。该领域之前的工作使用阈值或滤波器，且需要手动初始化。本文提出了一种基于主动外观模型（AAM）的自动化系统。我们的贡献在于实现了组织的完全自动分割以及创建AAM模型所需的半自动地标标定过程。使用215幅图像对AAM进行训练，并采用留一法进行测试。所得结果显示，皮质骨的初始化误差为3.25%，平均误差为1.63毫米；小梁核心的平均误差为2.90毫米；下颌管的平均误差为4.76毫米；牙神经的平均误差为3.40毫米。
