Geographic atrophy segmentation in SD-OCT images using synthesized fundus autofluorescence imaging. Computer methods and programs in biomedicine Wu, M. n., Cai, X. n., Chen, Q. n., Ji, Z. n., Niu, S. n., Leng, T. n., Rubin, D. L., Park, H. n. 2019; 182: 105101


Accurate assessment of geographic atrophy (GA) is critical for diagnosis and therapy of non-exudative age-related macular degeneration (AMD). Herein, we propose a novel GA segmentation framework for spectral-domain optical coherence tomography (SD-OCT) images that employs synthesized fundus autofluorescence (FAF) images.An en-face OCT image is created via the restricted sub-volume projection of three-dimensional OCT data. A GA region-aware conditional generative adversarial network is employed to generate a plausible FAF image from the en-face OCT image. The network balances the consistency between the entire synthesize FAF image and the lesion. We use a fully convolutional deep network architecture to segment the GA region using the multimodal images, where the features of the en-face OCT and synthesized FAF images are fused on the front-end of the network.Experimental results for 56 SD-OCT scans with GA indicate that our synthesis algorithm can generate high-quality synthesized FAF images and that the proposed segmentation network achieves a dice similarity coefficient, an overlap ratio, and an absolute area difference of 87.2%, 77.9%, and 11.0%, respectively.We report an automatic GA segmentation method utilizing synthesized FAF images.Our method is effective for multimodal segmentation of the GA region and can improve AMD treatment.

View details for DOI 10.1016/j.cmpb.2019.105101

View details for PubMedID 31600644