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. 2022 Sep 1;92(5):642-654.
doi: 10.2319/122121-928.1. Epub 2022 Jun 2.

Automated landmark identification on cone-beam computed tomography: Accuracy and reliability

Ali Ghowsi  1 , David Hatcher  2 , Heeyeon Suh  3 , David Wile  4 , Wesley Castro  5 , Jan Krueger  5 , Joorok Park  6 , Heesoo Oh  7
Affiliations

Automated landmark identification on cone-beam computed tomography: Accuracy and reliability

Ali Ghowsi et al. Angle Orthod. .

Abstract

Objectives: To evaluate the accuracy and reliability of a fully automated landmark identification (ALI) system as a tool for automatic landmark location compared with human judges.

Materials and methods: A total of 100 cone-beam computed tomography (CBCT) images were collected. After the calibration procedure, two human judges identified 53 landmarks in the x, y, and z coordinate planes on CBCTs using Checkpoint Software (Stratovan Corporation, Davis, Calif). The ground truth was created by averaging landmark coordinates identified by two human judges for each landmark. To evaluate the accuracy of ALI, the mean absolute error (mm) at the x, y, and z coordinates and mean error distance (mm) between the human landmark identification and the ALI were determined, and a successful detection rate was calculated.

Results: Overall, the ALI system was as successful at landmarking as the human judges. The ALI's mean absolute error for all coordinates was 1.57 mm on average. Across all three coordinate planes, 94% of the landmarks had a mean absolute error of less than 3 mm. The mean error distance for all 53 landmarks was 3.19 ± 2.6 mm. When applied to 53 landmarks on 100 CBCTs, the ALI system showed a 75% success rate in detecting landmarks within a 4-mm error distance range.

Conclusions: Overall, ALI showed clinically acceptable mean error distances except for a few landmarks. The ALI was more precise than humans when identifying landmarks on the same image at different times. This study demonstrates the promise of ALI in aiding orthodontists with landmark identifications on CBCTs.

Keywords: 3D landmark identification; Accuracy; Automated; CBCT; Landmark error; Reliability.

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Figures

Figure 1.
Figure 1.
Axial, sagittal, and coronal slices and 3D rendering views that were used when landmarking.
Figure 2.
Figure 2.
Mean error distance (mm) of all landmarks.
Figure 3.
Figure 3.
Scatterplots with 95% confidence ellipses depicting the envelope of error in different planes of view. Judge 1 is depicted in blue, Judge 2 is depicted in red, and ALI is depicted in green. For bilateral landmarks, the right-side landmark is presented. (A) Sella. (B) Nasion. (C) Basion. (D) Porion. (E) Orbitale. (F) ANS. (G) PNS. (H) A-Point. (I) B-Point. (J) Pogonion. (K) Menton. (L) Gonion. (M) UR1 Incisor Edge. (N) UR1 Root Apex. (O) LR1 Incisal Edge. (P) LR1 Root Apex.
Figure 3.
Figure 3.
Continued.
Figure 3.
Figure 3.
Continued.
Figure 3.
Figure 3.
Continued.
See this image and copyright information in PMC

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