Name
Title | ||
|---|---|---|
OVS-Net: An effective feature extraction network for optical coherence tomography angiography vessel segmentation |
Abstract | ||
|---|---|---|
Optical coherence tomography angiography (OCTA), as a noninvasive imaging modality, has been widely used in clinical ophthalmology. However, the segmentation of retinal vessels in OCTA is under-studied due to OCTA is a relatively new technology. In this article, an effective feature extraction network, OVS-Net, is proposed for OCTA vessel segmentation. The OVS-Net is divided into coarse stage and refine stage which structures are basically the same. In each stage, we utilize OctaveResBlock as the basic block to better extract the hierarchical multifrequency features of OCTA and capture the multiscale semantic features of the vessels. In order to improve the feature characterization, feature enhanced attention block is introduced into the network, which is proved to be more conducive for microvessel segmentation in our experiments. Multiscale feature blocks are introduced into the network to promote the deep integration of semantic features at different scales. Experiments on OCTA-SS and OCTA-500 datasets show that our proposed OVS-Net achieves more competitive segmentation results than the existing methods, especially for microvessel segmentation. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1002/cav.2096 | COMPUTER ANIMATION AND VIRTUAL WORLDS |
Keywords | DocType | Volume |
feature extraction, optical coherence tomography angiography, retinal vessel segmentation | Journal | 33 |
Issue | ISSN | Citations |
3-4 | 1546-4261 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chengzhang Zhu | 1 | 1 | 1.38 |
| Han Wang | 2 | 0 | 0.34 |
| Yalong Xiao | 3 | 0 | 0.34 |
| Yulan Dai | 4 | 0 | 0.34 |
| Zixi Liu | 5 | 0 | 0.34 |
| Beiji Zou | 6 | 231 | 41.61 |