Accuracy of computerized automatic identification of cephalometric landmarks by a designed software

Abstract

Objectives: The purpose of this study was to design software for localization of cephalometric landmarks and to evaluate its accuracy in finding landmarks. Methods: 40 digital cephalometric radiographs were randomly selected. 16 landmarks which were important in most cephalometric analyses were chosen to be identified. Three expert orthodontists manually identified landmarks twice. The mean of two measurements of each landmark was defined as the baseline landmark. The computer was then able to compare the automatic system's estimate of a landmark with the baseline landmark. The software was designed using Delphi and Matlab programming languages. The techniques were template matching, edge enhancement and some accessory techniques. Results: The total mean error between manually identified and automatically identified landmarks was 2.59 mm. 12.5% of landmarks had mean errors less than 1 mm. 43.75% of landmarks had mean errors less than 2 mm. The mean errors of all landmarks except the anterior nasal spine were less than 4 mm. Conclusions: This software had significant accuracy for localization of cephalometric landmarks and could be used in future applications. It seems that the accuracy obtained with the software which was developed in this study is better than previous automated systems that have used model-based and knowledge-based approaches.

Automatic three-dimensional cephalometric annotation system using three-dimensional convolutional neural networks: a developmental trial

“…They achieved an average accuracy of 2.01 mm, 64.67% of the landmarks falling within a range of 0 to 2 mm and the highest mean error in the linear measurements being 2.63 mm. Shahidi et al(Shahidi et al, 2013) Codari et al (Codari, Caffini, Tartaglia, Sforza, & Baselli, 2016;Codari et al, 2017) introduced a localization method using automatic segmentation and template-based non-rigid holistic registration which yielded an average localization error of 1.99 mm on 21 points. Finally, Montufar, J., et al (2018) (Montufar et al, 2018) used 24 conebeam CT scans and their orthogonal coronal and sagittal projections to obtain active shape model-based registration for 18 landmarks, achieving a 3.64-mm mean error; the highest localization errors were found in the porion and sella regions.…”

“…Implementations have drawn on various algorithms, including knowledge-based, model-based and machine learning-based approaches. (Codari, Caffini, Tartaglia, Sforza, & Baselli, 2017;Gupta, Kharbanda, Sardana, Balachandran, & Sardana, 2015;Makram & Kamel, 2014;Montufar, Romero, & Scougall-Vilchis, 2018;Shahidi, Oshagh, Gozin, Salehi, & Danaei, 2013) The knowledge-or model-based approaches were initially adopted due to their ease of development. However, their high ambiguity in identifying complex craniofacial structure hindered wider application.…”

Determination of Specific Anatomic Locations by SIFT Algorithm for Locating Cephalometric Points with the Use of an Automatic Technique

“…Lateral cephalometry is an important technique for the evaluation of growth, diagnosis, treatment planning and therapeutic evaluations (2). Three techniques are available for lateral cephalometry: manual technique, computer-aided technique and automatic technique (3,4).…”

“…The most important challenge in this technique is determination of landmarks (3,4). Different techniques have been used to solve this problem, which have generally been divided into 3 categories (5): 1) Edge-based or knowledge-based technique which consists of pre-processing and edge detecting steps plus the use of an algorithm to determine the landmarks (5).…”

On automatic landmarking

“…I read with interest the intriguing article of Shahidi et al, 1 which described in a good report the production of a rather effective computer program designed to identify lateral cephalometric landmarks automatically. The authors also tested its accuracy.…”