Name
Abstract | ||
|---|---|---|
Macular holes are a common eye condition which result in visual impairment. We look at the application of deep convolutional neural networks to the problem of macular hole segmentation. We use the 3D U-Net architecture as a basis and experiment with a number of design variants. Manually annotating and measuring macular holes is time consuming and error prone. Previous automated approaches to macular hole segmentation take minutes to segment a single 3D scan. Our proposed model generates significantly more accurate segmentations in less than a second. We found that an approach of architectural simplification, by greatly simplifying the network capacity and depth, exceeds both expert performance and state-of-the-art models such as residual 3D U-Nets. |
Year | Venue | DocType |
|---|---|---|
2021 | Irish Conference on Artificial Intelligence and Cognitive Science (AICS) | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jonathan Frawley | 1 | 0 | 0.68 |
| Chris G. Willcocks | 2 | 0 | 1.35 |
| Maged Habib | 3 | 7 | 1.02 |
| Caspar Geenen | 4 | 0 | 0.34 |
| David H. Steel | 5 | 0 | 0.34 |
| Boguslaw Obara | 6 | 145 | 17.81 |