Memory-efficient High-resolution OCT Volume Synthesis with Cascaded Amortized Latent Diffusion Models

TL;DR

This paper introduces a memory-efficient cascaded latent diffusion model for synthesizing high-resolution OCT volumes, enabling realistic data generation and improved segmentation performance in ophthalmology imaging tasks.

Contribution

The paper proposes a novel cascaded amortized latent diffusion model with autoencoders for efficient high-resolution OCT volume synthesis, surpassing existing methods in realism and downstream task performance.

Findings

Synthesizes realistic high-resolution OCT volumes

Improves segmentation accuracy on downstream tasks

Reduces memory and computational requirements

Abstract

Optical coherence tomography (OCT) image analysis plays an important role in the field of ophthalmology. Current successful analysis models rely on available large datasets, which can be challenging to be obtained for certain tasks. The use of deep generative models to create realistic data emerges as a promising approach. However, due to limitations in hardware resources, it is still difficulty to synthesize high-resolution OCT volumes. In this paper, we introduce a cascaded amortized latent diffusion model (CA-LDM) that can synthesis high-resolution OCT volumes in a memory-efficient way. First, we propose non-holistic autoencoders to efficiently build a bidirectional mapping between high-resolution volume space and low-resolution latent space. In tandem with autoencoders, we propose cascaded diffusion processes to synthesize high-resolution OCT volumes with a global-to-local refinement process, amortizing the memory and computational demands. Experiments on a public high-resolution OCT dataset show that our synthetic data have realistic high-resolution and global features, surpassing the capabilities of existing methods. Moreover, performance gains on two down-stream fine-grained segmentation tasks demonstrate the benefit of the proposed method in training deep learning models for medical imaging tasks. The code is public available at: https://github.com/nicetomeetu21/CA-LDM.