Proposition of local automatic algorithm for landmark detection in 3D cephalometry

Abstract

This study proposes a new contribution to solve the problem of automatic landmarks detection in three-dimensional cephalometry. 3D images obtained from CBCT (cone beam computed tomography) equipment were used for automatic identification of twelve landmarks. The proposed method is based on a local geometry and intensity criteria of skull structures. After the step of preprocessing and binarization, the algorithm segments the skull into three structures using the geometry information of nasal cavity and intensity information of the teeth. Each targeted landmark was detected using local geometrical information of the volume of interest containing this landmark. The ICC and confidence interval (95% CI) for each direction were 0, 91 (0.75 to 0.96) for x- direction; 0.92 (0.83 to 0.97) for y-direction; 0.92 (0.79 to 0.97) for z-direction. The mean error of detection was calculated using the Euclidian distance between the 3D coordinates of manually and automatically detected landmarks. The overall mean error of the algorithm was 2.76 mm with a standard deviation of 1.43 mm. Our proposed approach for automatic landmark identification in 3D cephalometric was capable of detecting 12 landmarks on 3D CBCT images which can be facilitate the use of 3D cephalometry to orthodontists.

Three-dimensional cephalometric analysis of virtual dentoskeletal model

“…Ed-dhahraouy et al [38] an automatic landmarks detection method proposed based on local geometry and intensity standards of the structure of the skull.…”

Threshold-Based Segmentation for Landmark Detection Using CBCT Images

“…A few studies have attempted to provide algorithms for automatic or semiautomatic landmark detection in 3D cephalometry [4][5][6][7], [12][13]. The aim of this study is to show the influence of threshold-based segmentation on automatic landmark detection.…”

“…Recently, many research groups have addressed the use of CBCT in 3D cephalometry to overcome the limitations of 2D cephalometry [1][2][3]. A few researchers have proposed automatic and semi-automatic algorithms for landmark detection in 3D cephalometry [4][5][6][7], [9][10][11][12]. In the literature, there are three main methods employed for automatic 3D cephalometry: model-based [4][5]7], deep-learning [13], and knowledge-based strategies [6,9,12].…”

“…A few researchers have proposed automatic and semi-automatic algorithms for landmark detection in 3D cephalometry [4][5][6][7], [9][10][11][12]. In the literature, there are three main methods employed for automatic 3D cephalometry: model-based [4][5]7], deep-learning [13], and knowledge-based strategies [6,9,12]. Convolutional neural networks (CNNs) have shown great potential in medical image analysis, especially in landmark detection.…”