Latent Diffusion with Anatomical Guidance for Retinal Thickness Map Synthesis
Abstract
Abstract Objective: Retinal thickness is an important structural biomarker for the assessment and monitoring of retinal diseases. In routine practice, thickness measurements are obtained from Optical Coherence Tomography (OCT), which provides depth-resolved retinal imaging but may not be available in all clinical settings. This study investigates whether high-fidelity Retinal Thickness Maps (RTMs) can be synthesized directly from widely available Color Fundus Photographs (CFPs) using paired CFP–RTM data. Methods: We propose an anatomically guided latent diffusion framework for cross-modal retinal topography synthesis.The framework builds upon latent diffusion generative modeling[1] and employs a perceptually compressed latent representation learned with a Variational Autoencoder (VAE)[2]. To better preserve modality-relevant retinal structure during conditional generation, we incorporate a Spatially-Weighted Cross-Attention (SW-CA) module that applies anatomically informed spatial weighting within the attention mechanism, inspired by attention-based conditioning strategies used in recent vision architectures[3, 4]. Model performance was evaluated using commonly adopted image fidelity and perceptual metrics, including PSNR, SSIM, LPIPS, and FID [5–8], and compared against representative convolutional, generative adversarial, and transformer-based image synthesis approaches. Results: Across quantitative evaluations, the proposed AC-LDM achieved improved reconstruction fidelity…
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