Title | ||
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Automated geographic atrophy segmentation for SD-OCT images based on two-stage learning model. |
Abstract | ||
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Automatic and reliable segmentation for geographic atrophy in spectral-domain optical coherence tomography (SD-OCT) images is a challenging task. To develop an effective segmentation method, a two-stage deep learning framework based on an auto-encoder is proposed. Firstly, the axial data of cross-section images were used as samples instead of the projection images of SD-OCT images. Next, a two-stage learning model that includes offline-learning and self-learning was designed based on a stacked sparse auto-encoder to obtain deep discriminative representations. Finally, a fusion strategy was used to refine the segmentation results based on the two-stage learning results. The proposed method was evaluated on two datasets consisting of 55 and 56 cubes, respectively. For the first dataset, our method obtained a mean overlap ratio (OR) of 89.85 ± 6.35% and an absolute area difference (AAD) of 4.79 ± 7.16%. For the second dataset, the mean OR and AAD were 84.48 ± 11.98%, 11.09 ± 13.61%, respectively. Compared with the state-of-the-art algorithms, experiments indicate that the proposed algorithm can provide more accurate segmentation results on these two datasets without using retinal layer segmentation. |
Year | DOI | Venue |
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2019 | 10.1016/j.compbiomed.2018.12.013 | Computers in Biology and Medicine |
Keywords | Field | DocType |
Image segmentation,Spectral-domain optical coherence tomography,Geographic atrophy,Stack sparse auto-encoder,Deep learning | Computer vision,Optical coherence tomography,Pattern recognition,Computer science,Segmentation,Artificial intelligence,Deep learning,Atrophy,Discriminative model,Cube | Journal |
Volume | ISSN | Citations |
105 | 0010-4825 | 0 |
PageRank | References | Authors |
0.34 | 14 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Rongbin Xu | 1 | 37 | 10.01 |
Sijie Niu | 2 | 47 | 10.94 |
Qiang Chen | 3 | 604 | 57.44 |
Zexuan Ji | 4 | 459 | 26.03 |
Daniel L. Rubin | 5 | 1645 | 145.14 |
Yuehui Chen | 6 | 1167 | 106.13 |