Abstract
Automatic annotation for three-dimensional (3D) cephalometric analysis has been limited by computational complexity and computing performance. The purpose of this study was to evaluate the accuracy of our newly-developed automatic 3D cephalometric annotation system using a deep learning algorithm. Our model system mainly consisted of a 3D convolutional neural network and image data resampling. Discrepancies between the referenced and predicted coordinate values in three axes and in 3D distance were calculated to yield 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 (p > 0.05) among the landmarks of three groups (midsagittal plane, horizontal plane and mandible). Although our 3D convolutional neural network-based annotation system could not achieve the level of accuracy demanded by immediate clinical applications, it can nevertheless serve as an initial approximate guide to landmarks, thus reducing the time needed for annotation.
| Original language | English |
|---|---|
| Journal | Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization |
| DOIs | |
| Publication status | Accepted/In press - 2019 Jan 1 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Computational Mechanics
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging
- Computer Science Applications
Cite this
}
Automatic three-dimensional cephalometric annotation system using three-dimensional convolutional neural networks : a developmental trial. / Kang, Sung Ho; Jeon, Kiwan; Kim, Hak Jin; Seo, Jin Keun; Lee, Sang Hwy.
In: Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 01.01.2019.Research output: Contribution to journal › Article
TY - JOUR
T1 - Automatic three-dimensional cephalometric annotation system using three-dimensional convolutional neural networks
T2 - a developmental trial
AU - Kang, Sung Ho
AU - Jeon, Kiwan
AU - Kim, Hak Jin
AU - Seo, Jin Keun
AU - Lee, Sang Hwy
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Automatic annotation for three-dimensional (3D) cephalometric analysis has been limited by computational complexity and computing performance. The purpose of this study was to evaluate the accuracy of our newly-developed automatic 3D cephalometric annotation system using a deep learning algorithm. Our model system mainly consisted of a 3D convolutional neural network and image data resampling. Discrepancies between the referenced and predicted coordinate values in three axes and in 3D distance were calculated to yield 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 (p > 0.05) among the landmarks of three groups (midsagittal plane, horizontal plane and mandible). Although our 3D convolutional neural network-based annotation system could not achieve the level of accuracy demanded by immediate clinical applications, it can nevertheless serve as an initial approximate guide to landmarks, thus reducing the time needed for annotation.
AB - Automatic annotation for three-dimensional (3D) cephalometric analysis has been limited by computational complexity and computing performance. The purpose of this study was to evaluate the accuracy of our newly-developed automatic 3D cephalometric annotation system using a deep learning algorithm. Our model system mainly consisted of a 3D convolutional neural network and image data resampling. Discrepancies between the referenced and predicted coordinate values in three axes and in 3D distance were calculated to yield 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 (p > 0.05) among the landmarks of three groups (midsagittal plane, horizontal plane and mandible). Although our 3D convolutional neural network-based annotation system could not achieve the level of accuracy demanded by immediate clinical applications, it can nevertheless serve as an initial approximate guide to landmarks, thus reducing the time needed for annotation.
UR - http://www.scopus.com/inward/record.url?scp=85074321214&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074321214&partnerID=8YFLogxK
U2 - 10.1080/21681163.2019.1674696
DO - 10.1080/21681163.2019.1674696
M3 - Article
AN - SCOPUS:85074321214
JO - Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
JF - Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
SN - 2168-1163
ER -