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.
|Number of pages||9|
|Journal||Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization|
|Publication status||Published - 2020 Mar 3|
Bibliographical noteFunding Information:
This work was supported by the [Funding Agency #1] National Institute for Mathematical Sciences (NIMS) grant funded by the Korean government [No. NIMS-B19610000] for S H Kang and K W Jeon; [Funding Agency #2] a grant of the Korea Health Technology R&D Project funded by the Ministry of Health & Welfare, Republic of Korea [HI17C0177] for S?H Lee.
All Science Journal Classification (ASJC) codes
- Computational Mechanics
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging
- Computer Science Applications