Name
Abstract | ||
|---|---|---|
Automated localization of optic disc and fovea is important for computer-aided retinal disease screening and diagnosis. Compared to previous works, this paper makes two novelties. First, we study the localization problem in the new context of ultra-widefield (UWF) fundus images, which has not been considered before. Second, we propose a spatially constrained Faster R-CNN for the task. Extensive experiments on a set of 2,182 UWF fundus images acquired from a local eye center justify the viability of the proposed model. For more than 99% of the test images, the improved Faster R-CNN localizes the fovea within one optic disc diameter to the ground truth, meanwhile detecting the optic disc with a high IoU of 0.82. The new model works reasonably well even in challenging cases where the fovea is occluded due to severe retinopathy or surgical treatments. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-32692-0_52 | Lecture Notes in Computer Science |
Keywords | DocType | Volume |
Object localization,UWF fundus image,Faster R-CNN | Conference | 11861 |
ISSN | Citations | PageRank |
0302-9743 | 1 | 0.37 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhuoya Yang | 1 | 1 | 0.37 |
| Xirong Li | 2 | 1191 | 68.62 |
| Xixi He | 3 | 2 | 1.09 |
| Dayong Ding | 4 | 3 | 2.47 |
| Yanting Wang | 5 | 1 | 0.37 |
| Fangfang Dai | 6 | 1 | 0.37 |
| Xuemin Jin | 7 | 1 | 0.37 |