Name
Abstract | ||
|---|---|---|
•A boundary-guided convolutional neural network (BG-CNN) was proposed to accurately and simultaneously segment different corneal layers and delineate their boundaries from OCT images.•Two network modules were defined based on the classical U-Net network by introducing three different convolutional blocks.•Experiment results on our collected OCT images demonstrated that the developed network achieved reasonable performance to identify corneal layers, as compared with several available networks. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2021.108158 | Pattern Recognition |
Keywords | DocType | Volume |
Corneal layers,OCT images,Segmentation,Convolutional neural networks | Journal | 120 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Wang | 1 | 947 | 61.46 |
| Meixiao Shen | 2 | 7 | 1.52 |
| Qian Chang | 3 | 0 | 0.34 |
| Ce Shi | 4 | 0 | 1.35 |
| Chen Yang | 5 | 172 | 43.55 |
| Yuheng Zhou | 6 | 0 | 0.34 |
| Yanchun Zhang | 7 | 3059 | 284.90 |
| Jiantao Pu | 8 | 277 | 23.12 |
| Hao Chen | 9 | 1 | 1.11 |