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基于相关投影中活动形状模型的自动三维头影测量地标定位。

Automatic 3-dimensional cephalometric landmarking based on active shape models in related projections.

作者信息

Montúfar Jesús, Romero Marcelo, Scougall-Vilchis Rogelio J

机构信息

Department of Engineering, Universidad Autónoma del Estado de México, Toluca, Mexico.

Department of Engineering, Universidad Autónoma del Estado de México, Toluca, Mexico.

出版信息

Am J Orthod Dentofacial Orthop. 2018 Mar;153(3):449-458. doi: 10.1016/j.ajodo.2017.06.028.

Abstract

INTRODUCTION

This article presents a novel technique for automatic cephalometric landmark localization on 3-dimensional (3D) cone-beam computed tomography (CBCT) volumes by using an active shape model to search for landmarks in related projections.

METHODS

Twenty-four random CBCT scans from a public data set were imported and processed into Matlab (MathWorks, Natick, Mass). Orthogonal coronal and sagittal projections (digitally reconstructed radiographs) were created, and 2 trained active shape models were used to locate cephalometric landmarks on each. Finally, by relating projections, 18 tridimensional landmarks were located on CBCT volume representations.

RESULTS

From our 3D gold standard, a 3.64-mm mean error in localization of cephalometric landmarks was achieved with this method, with the highest localization errors in the porion and sella regions because of the low volume definition.

CONCLUSIONS

The proposed algorithm for automatic 3D landmarking on CBCT volumes seems to be useful for 3D cephalometric analysis. This study shows that a fast 2-dimensional landmark search can be useful for 3D localization, which could save computational time compared with a full-volume analysis. Also, this research confirms that by using CBCT for cephalometry, there are no distortion projections, and full structure information of a virtual patient is manageable in a personal computer.

摘要

引言

本文介绍了一种通过使用主动形状模型在相关投影中搜索标志点,从而在三维(3D)锥形束计算机断层扫描(CBCT)容积上自动进行头影测量标志点定位的新技术。

方法

从一个公共数据集中导入24例随机的CBCT扫描数据,并将其处理后导入Matlab(MathWorks公司,马萨诸塞州纳蒂克)。创建正交冠状面和矢状面投影(数字重建X线片),并使用2个经过训练的主动形状模型在每个投影上定位头影测量标志点。最后,通过关联投影,在CBCT容积图像上定位18个三维标志点。

结果

与我们的3D金标准相比,该方法对头影测量标志点定位的平均误差为3.64毫米,由于容积定义较低,在耳点和蝶鞍区域的定位误差最高。

结论

所提出的在CBCT容积上自动进行三维标志点定位的算法似乎对头影测量分析有用。本研究表明,快速的二维标志点搜索对于三维定位是有用的,与全容积分析相比,可以节省计算时间。此外,本研究证实,通过使用CBCT进行头影测量,不存在投影失真,并且虚拟患者的完整结构信息在个人计算机上是可管理的。

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