Name
Title | ||
|---|---|---|
Highly Accurate Facial Nerve Segmentation Refinement From CBCT/CT Imaging Using a Super-Resolution Classification Approach. |
Abstract | ||
|---|---|---|
Facial nerve segmentation is of considerable importance for preoperative planning of cochlear implantation. However, it is strongly influenced by the relatively low resolution of the cone-beam computed tomography (CBCT) images used in clinical practice. In this paper, we propose a super-resolution classification method, which refines a given initial segmentation of the facial nerve to a subvoxel c... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TBME.2017.2697916 | IEEE Transactions on Biomedical Engineering |
Keywords | Field | DocType |
Image resolution,Feature extraction,Image segmentation,Computed tomography,Manuals,Bones | Computer vision,Scale-space segmentation,Computer science,Sørensen–Dice coefficient,Facial nerve,Segmentation,Image segmentation,Feature extraction,Artificial intelligence,Hausdorff distance,Image resolution | Journal |
Volume | Issue | ISSN |
65 | 1 | 0018-9294 |
Citations | PageRank | References |
2 | 0.42 | 17 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ping Lu | 1 | 2 | 0.42 |
| Livia Barazzetti | 2 | 4 | 1.49 |
| Vimal Chandran | 3 | 2 | 0.42 |
| Kate Gavaghan | 4 | 37 | 7.06 |
| Stefan Weber | 5 | 39 | 12.46 |
| Nicolas Gerber | 6 | 44 | 9.79 |
| Mauricio Reyes | 7 | 459 | 25.89 |