TPOT: Topology Preserving Optimal Transport in Retinal Fundus Image Enhancement

TL;DR

A topology-preserving training paradigm that regularizes blood vessel structures by minimizing the differences of persistence diagrams is proposed and called Topology Preserving Optimal Transport (TPOT).

Abstract

Retinal fundus photography enhancement is important for diagnosing and monitoring retinal diseases. However, early approaches to retinal image enhancement, such as those based on Generative Adversarial Networks (GANs), often struggle to preserve the complex topological information of blood vessels, resulting in spurious or missing vessel structures. The persistence diagram, which captures topological features based on the persistence of topological structures under different filtrations, provides a promising way to represent the structure information. In this work, we propose a topology-preserving training paradigm that regularizes blood vessel structures by minimizing the differences of persistence diagrams. We call the resulting framework Topology Preserving Optimal Transport (TPOT). Experimental results on a large-scale dataset demonstrate the superiority of the proposed method compared to several state-of-the-art supervised and unsupervised techniques, both in terms of image quality and performance in the downstream blood vessel segmentation task. The code is available at https://github.com/Retinal-Research/TPOT.

Figures (2)

• Figure 1: (A) illustrates the TPOT framework. The structures of $G_\theta$ and $D_\beta$ follows the design outlined in zhu2023otrezhu2023optimal, and $S_N$ follows the design presented in zhou2021study. (B) represents the changes in segmentation masks of the images during training. The orange box highlights regions with complex topological structures. (C) provides an example of how our topology-preserving regularization operates. The yellow crosses and blue dots represent corresponding persistent features in $S_N(\mathbf{x})$ and $S_N(G_\theta(\mathbf{x}))$, respectively. The green dots indicate persistent noise features that must be removed during training. The model penalizes the differences between corresponding points to encourage topological consistency. Further details are discussed in Sec. \ref{['sec:topo-regularization']}.
• Figure 2: (A) illustrates the result of quality enhancement task on the EyeQ dataset. (B) shows the enhanced DRIVE image along with the corresponding vessel segmentation. The orange box highlights a patch with a complex topological structure, where our method demonstrates greater consistency.