Geographic atrophy segmentation in SD-OCT images using synthesized fundus autofluorescence imaging

Comput Methods Programs Biomed. 2019 Dec;182:105101. doi: 10.1016/j.cmpb.2019.105101. Epub 2019 Sep 28.

Abstract

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.

Keywords: Biomedical image segmentation; Geographic atrophy; Image synthesis; Optical coherence tomography; Retinal image analysis.

MeSH terms

  • Automation
  • Fundus Oculi*
  • Geographic Atrophy / diagnostic imaging*
  • Humans
  • Optical Imaging / methods*
  • Tomography, Optical Coherence / methods*