BlastDiffusion: A Latent Diffusion Model for Generating Synthetic Embryo Images to Address Data Scarcity in In Vitro Fertilization

TL;DR

BlastDiffusion is a novel latent diffusion model that generates realistic embryo images conditioned on developmental outcomes, improving data availability for reproductive diagnostics.

Contribution

We introduce BlastDiffusion, a diffusion-based generative model that outperforms GANs in synthesizing embryo images for reproductive medicine applications.

Findings

Achieves a lower FID score of 94.32 compared to 232.73 for GAN-based models.

Shows improved perceptual and structural similarity to real embryo images.

Captures key morphological differences linked to embryo development outcomes.

Abstract

Accurately identifying oocytes that progress to the blastocyst stage is crucial in reproductive medicine, but the limited availability of annotated high-quality embryo images presents challenges for developing automated diagnostic tools. To address this, we propose BlastDiffusion, a generative model based on Latent Diffusion Models (LDMs) that synthesizes realistic oocyte images conditioned on developmental outcomes. Our approach utilizes a pretrained Variational Autoencoder (VAE) for latent space representation, combined with a diffusion process to generate images that distinguish between oocytes that reach the blastocyst stage and those that do not. When compared to Blastocyst-GAN, a GAN-based model we trained for this task, BlastDiffusion achieves superior performance, with a global Frechet Inception Distance (FID) of 94.32, significantly better than Blastocyst-GAN's FID of 232.73. Additionally, our model shows improvements in perceptual (LPIPS) and structural (SSIM) similarity to real oocyte images. Qualitative analysis further demonstrates that BlastDiffusion captures key morphological differences linked to developmental outcomes. These results highlight the potential of diffusion models in reproductive medicine, offering an effective tool for data augmentation and automated embryo assessment.