Giordano 32.58 University of Catania Francesco Maiorana 18.39 University of Catania Concetto Spampinato 32.52 University of Catania Request full-text PDF To read the article of this research, you can request a copy directly from the authors.
Cephalometric Points Download Citation CopyRequest full-text Download citation Copy link Link copied Request full-text Download citation Copy link Link copied To read the full-text of this research, you can request a copy directly from the authors.Citations (62) References (47) Abstract To describe the techniques used for automatic landmarking of cephalograms, highlighting the strengths and weaknesses of each one and reviewing the percentage of success in locating each cephalometric point.The literature survey was performed by searching the Medline, the Institute of Electrical and Electronics Engineers, and the ISI Web of Science Citation Index databases.
The survey covered the period from January 1966 to August 2006. Abstracts that appeared to fulfill the initial selection criteria were selected by consensus. The search strategy resulted in 118 articles of which eight met the inclusion criteria. Many articles were rejected for different reasons; among these, the most frequent was that results of accuracy for automatic landmark recognition were presented as a percentage of success. A marked difference in results was found between the included studies consisting of heterogeneity in the performance of techniques to detect the same landmark. All in all, hybrid approaches detected cephalometric points with a higher accuracy in contrast to the results for the same points obtained by the model-based, image filtering plus knowledge-based landmark search and soft-computing approaches. The systems described in the literature are not accurate enough to allow their use for clinical purposes. Cephalometric Points Manual Tracing AndErrors in landmark detection were greater than those expected with manual tracing and, therefore, the scientific evidence supporting the use of automatic landmarking is low. Cephalometric Points For Free No FullDiscover the worlds research 17 million members 135 million publications 700k research projects Join for free No full-text available Request the article directly from the authors on ResearchGate. Our 2D distance error results indicate less distortion than in previous 2D cephalometric annotation reports (Kaur Singh, 2015; Leonardi et al., 2008; Shahidi et al., 2013;Tanikawa et al., 2009), and hold promise for further improving the model system for clinical settings.. Automatic Three-Dimensional Cephalometric Annotation System Using Three-Dimensional Convolutional Neural Networks Preprint Nov 2018 Sung Ho Kang Kiwan Jeon Hak-Jin Kim Sang-Hwy Lee Background: Three-dimensional (3D) cephalometric analysis using computerized tomography data has been rapidly adopted for dysmorphosis and anthropometry. Several different approaches to automatic 3D annotation have been proposed to overcome the limitations of traditional cephalometry. The purpose of this study was to evaluate the accuracy of our newly-developed system using a deep learning algorithm for automatic 3D cephalometric annotation. Our deep learning-based model system mainly consisted of a 3D convolutional neural network and image data resampling. Results: The discrepancies between the referenced and predicted coordinate values in three axes and in 3D distance were calculated to evaluate system accuracy. Our new model system yielded prediction errors of 3.26, 3.18, and 4.81 mm (for three axes) and 7.61 mm (for 3D). Moreover, there was no difference among the landmarks of the three groups, including the midsagittal plane, horizontal plane, and mandible (p0.05). Conclusion: A new 3D convolutional neural network-based automatic annotation system for 3D cephalometry was developed. The strategies used to implement the system were detailed and measurement results were evaluated for accuracy. Further development of this system is planned for full clinical application of automatic 3D cephalometric annotation. Research stated that digital methods can be also lead to some errors such as transferring, magnification, and measurements errors.
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