TPOT: Topology Preserving Optimal Transport in Retinal Fundus Image Enhancement
This work addresses the challenge of maintaining accurate blood vessel structures in retinal images for medical diagnosis, representing an incremental improvement over existing enhancement techniques.
The paper tackles the problem of preserving complex topological information of blood vessels in retinal fundus image enhancement, which is crucial for diagnosing retinal diseases, by proposing a topology-preserving training paradigm that minimizes differences in persistence diagrams, resulting in improved image quality and downstream blood vessel segmentation performance compared to state-of-the-art methods.
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.