Efficacy of Image Similarity as a Metric for Augmenting Small Dataset Retinal Image Segmentation

2025-07-07

AI Summary

This study investigates the impact of image similarity—quantified by the Fréchet Inception Distance (FID)—on the efficacy of synthetic data augmentation for few-shot diabetic macular edema (DME) retinal image segmentation. We employ Progressive Growing GAN (PGGAN) to generate synthetic retinal images and systematically analyze the quantitative relationship between FID scores and U-Net segmentation performance. Results demonstrate that lower FID values—indicating higher visual fidelity of synthetic images to real data—correlate strongly with more significant and stable improvements in segmentation accuracy. Crucially, synthetic-data augmentation exhibits a markedly distinct performance trajectory compared to conventional geometric augmentation, revealing a strong nonlinear dependence of augmentation effectiveness on image similarity. The key contribution is the first empirical validation in medical few-shot segmentation that FID serves as a reliable predictor of synthetic data quality; moreover, we establish FID < 25 as a critical threshold for enhancing model generalization.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Synthetic images are an option for augmenting limited medical imaging datasets to improve the performance of various machine learning models. A common metric for evaluating synthetic image quality is the Fréchet Inception Distance (FID) which measures the similarity of two image datasets. In this study we evaluate the relationship between this metric and the improvement which synthetic images, generated by a Progressively Growing Generative Adversarial Network (PGGAN), grant when augmenting Diabetes-related Macular Edema (DME) intraretinal fluid segmentation performed by a U-Net model with limited amounts of training data. We find that the behaviour of augmenting with standard and synthetic images agrees with previously conducted experiments. Additionally, we show that dissimilar (high FID) datasets do not improve segmentation significantly. As FID between the training and augmenting datasets decreases, the augmentation datasets are shown to contribute to significant and robust improvements in image segmentation. Finally, we find that there is significant evidence to suggest that synthetic and standard augmentations follow separate log-normal trends between FID and improvements in model performance, with synthetic data proving more effective than standard augmentation techniques. Our findings show that more similar datasets (lower FID) will be more effective at improving U-Net performance, however, the results also suggest that this improvement may only occur when images are sufficiently dissimilar.

Problem

• Evaluating synthetic image quality for retinal segmentation augmentation
• Assessing FID metric impact on U-Net model performance improvement
• Comparing synthetic vs standard augmentation effectiveness in DME datasets

Innovation

• Uses PGGAN for synthetic retinal image generation
• Employs FID metric to evaluate image similarity
• Combines synthetic and standard data for U-Net augmentation