Name
Title | ||
|---|---|---|
Automated segmentation of the optic disc from fundus images using an asymmetric deep learning network |
Abstract | ||
|---|---|---|
•A novel deep learning network was proposed based on the classical U-Net model to accurately segment the optic disc from colour fundus images.•A sub-network and a decoding convolutional block were introduced to provide additional key features and highlight the morphological changes of the target objects in convolutional feature maps.•Experiment results on both the global field-of-view fundus images and their local disc versions from the MESSIDOR, ORIGA, and REFUGE datasets demonstrated that the developed network achieved promising performance and outperformed some existing segmentation networks. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2020.107810 | Pattern Recognition |
Keywords | DocType | Volume |
Segmentation,Colour fundus images,Optic disc,Deep learning,U-Net | Journal | 112 |
Issue | ISSN | Citations |
1 | 0031-3203 | 1 |
PageRank | References | Authors |
0.43 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Wang | 1 | 947 | 61.46 |
| Juan Gu | 2 | 1 | 0.43 |
| Yize Chen | 3 | 1 | 0.43 |
| Yuanbo Liang | 4 | 1 | 0.43 |
| Weijie Zhang | 5 | 1 | 0.43 |
| Jiantao Pu | 6 | 277 | 23.12 |
| Hao Chen | 7 | 1 | 1.11 |