Publication Type:Journal Article
Source:Comput Biol Med, Volume 105, p.102-111 (2019)
ISBN:1879-0534 (Electronic)<br/>0010-4825 (Linking)
Keywords:Deep Learning, geographic atrophy, image segmentation, spectral-domain optical coherence tomography, Stack sparse auto-encoder
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.