Name
Abstract | ||
|---|---|---|
Segmenting optical coherence tomography (OCT) images of the retina is important in the diagnosis, staging, and tracking of ophthalmological diseases. Whereas automatic segmentation methods are typically much faster than manual segmentation, they may still take several minutes to segment a three-dimensional macular scan, and this can be prohibitive for routine clinical application. In this paper, we propose a fast, multi-layer macular OCT segmentation method based on a fast level set method. In our framework, the boundary evolution operations are computationally fast, are specific to each boundary between retinal layers, guarantee proper layer ordering, and avoid level set computation during evolution. Subvoxel resolution is achieved by reconstructing the level set functions after convergence. Experiments demonstrate that our method reduces the computation expense by 90% compared to graph-based methods and produces comparable accuracy to both graph-based and level set retinal OCT segmentation methods. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ISBI.2018.8363844 | 2018 IEEE 15TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2018) |
Keywords | Field | DocType |
fast level set method, OCT, multi-object segmentation, topology preservation | Convergence (routing),Computer vision,Optical coherence tomography,Multi layer,Pattern recognition,Computer science,Level set method,Segmentation,Level set,Level set segmentation,Artificial intelligence,Computation | Conference |
Volume | ISSN | Citations |
2018 | 1945-7928 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yihao Liu | 1 | 6 | 3.86 |
| Aaron Carass | 2 | 383 | 43.15 |
| Sharon D. Solomon | 3 | 11 | 4.29 |
| shiv saidha | 4 | 5 | 3.49 |
| Peter A. Calabresi | 5 | 232 | 20.40 |
| Jerry L. Prince | 6 | 4990 | 488.42 |