Name
Title | ||
|---|---|---|
Localization of Craniomaxillofacial Landmarks on CBCT Images Using 3D Mask R-CNN and Local Dependency Learning |
Abstract | ||
|---|---|---|
Cephalometric analysis relies on accurate detection of craniomaxillofacial (CMF) landmarks from cone-beam computed tomography (CBCT) images. However, due to the complexity of CMF bony structures, it is difficult to localize landmarks efficiently and accurately. In this paper, we propose a deep learning framework to tackle this challenge by jointly digitalizing 105 CMF landmarks on CBCT images. By explicitly learning the local geometrical relationships between the landmarks, our approach extends Mask R-CNN for end-to-end prediction of landmark locations. Specifically, we first apply a detection network on a down-sampled 3D image to leverage global contextual information to predict the approximate locations of the landmarks. We subsequently leverage local information provided by higher-resolution image patches to refine the landmark locations. On patients with varying non-syndromic jaw deformities, our method achieves an average detection accuracy of 1.38± 0.95mm, outperforming a related state-of-the-art method. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TMI.2022.3174513 | IEEE Transactions on Medical Imaging |
Keywords | DocType | Volume |
Craniomaxilloficial (CMF) landmark localization,deep learning,Mask R-CNN | Journal | 41 |
Issue | ISSN | Citations |
10 | 0278-0062 | 0 |
PageRank | References | Authors |
0.34 | 0 | 13 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yankun Lang | 1 | 0 | 2.03 |
| Chunfeng Lian | 2 | 0 | 0.34 |
| Deqiang Xiao | 3 | 4 | 4.50 |
| Hannah Deng | 4 | 9 | 2.20 |
| Kim-Han Thung | 5 | 0 | 0.34 |
| Peng Yuan | 6 | 0 | 0.34 |
| Jaime Gateno | 7 | 0 | 0.68 |
| Tianshu Kuang | 8 | 6 | 5.24 |
| David M Alfi | 9 | 0 | 0.34 |
| Li Wang | 10 | 1051 | 78.25 |
| Dinggang Shen | 11 | 0 | 0.34 |
| James J. Xia | 12 | 64 | 16.52 |
| Pew-Thian Yap | 13 | 1093 | 93.77 |