  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.
