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Updated: Jan 6, 2026
Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases
Published on: March 11, 2016
Geographic atrophy segmentation in SD-OCT images using synthesized fundus autofluorescence imaging
Menglin Wu1, Xinxin Cai1, Qiang Chen2
1School of Computer Science and Technology, Nanjing Tech University, Nanjing, China.
Background And Objective:
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.
Methods:
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.
Results:
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.
Conclusion:
We report an automatic GA segmentation method utilizing synthesized FAF images.
Significance:
Our method is effective for multimodal segmentation of the GA region and can improve AMD treatment.
