Active Diffusion Matching: Score-based Iterative Alignment of Cross-Modal Retinal Images

TL;DR

Active Diffusion Matching (ADM) is a novel score-based iterative method that accurately aligns cross-modal retinal images, addressing a significant challenge in ophthalmology imaging analysis.

Contribution

The paper introduces ADM, a new approach using score-based diffusion models for joint global and local alignment of cross-modal retinal images.

Findings

ADM achieves state-of-the-art accuracy on private and public datasets.

ADM improves alignment accuracy with mAUC gains of 5.2 and 0.4 points.

The method effectively bridges the gap between different retinal imaging modalities.

Abstract

Objective: The study aims to address the challenge of aligning Standard Fundus Images (SFIs) and Ultra-Widefield Fundus Images (UWFIs), which is difficult due to their substantial differences in viewing range and the amorphous appearance of the retina. Currently, no specialized method exists for this task, and existing image alignment techniques lack accuracy. Methods: We propose Active Diffusion Matching (ADM), a novel cross-modal alignment method. ADM integrates two interdependent score-based diffusion models to jointly estimate global transformations and local deformations via an iterative Langevin Markov chain. This approach facilitates a stochastic, progressive search for optimal alignment. Additionally, custom sampling strategies are introduced to enhance the adaptability of ADM to given input image pairs. Results: Comparative experimental evaluations demonstrate that ADM achieves state-of-the-art alignment accuracy. This was validated on two datasets: a private dataset of SFI-UWFI pairs and a public dataset of SFI-SFI pairs, with mAUC improvements of 5.2 and 0.4 points on the private and public datasets, respectively, compared to existing state-of-the-art methods. Conclusion: ADM effectively bridges the gap in aligning SFIs and UWFIs, providing an innovative solution to a previously unaddressed challenge. The method's ability to jointly optimize global and local alignment makes it highly effective for cross-modal image alignment tasks. Significance: ADM has the potential to transform the integrated analysis of SFIs and UWFIs, enabling better clinical utility and supporting learning-based image enhancements. This advancement could significantly improve diagnostic accuracy and patient outcomes in ophthalmology.