TPOT：视网膜眼底图像增强中的拓扑保持最优传输
TPOT: Topology Preserving Optimal Transport in Retinal Fundus Image Enhancement
AI总结：
针对视网膜眼底图像增强中血管拓扑信息易丢失的问题，提出基于持久性图差异最小化的拓扑保持最优传输（TPOT）训练范式，在大型数据集上验证了其在图像质量和下游分割任务上的优越性。
摘要：
视网膜眼底摄影增强对于诊断和监测视网膜疾病至关重要。然而，早期的视网膜图像增强方法，例如基于生成对抗网络（GANs）的方法，往往难以保留血管复杂的拓扑信息，导致出现虚假或缺失的血管结构。持久性图通过在不同过滤条件下捕获拓扑结构的持续性来表征拓扑特征，为表示结构信息提供了一种有前景的方式。在这项工作中，我们提出了一种拓扑保持的训练范式，通过最小化持久性图之间的差异来正则化血管结构。我们将由此产生的框架称为拓扑保持最优传输（TPOT）。在大型数据集上的实验结果表明，与几种最先进的监督和无监督技术相比，所提出的方法在图像质量和下游血管分割任务的性能方面均具有优越性。代码可在 https://github.com/Retinal-Research/TPOT 获取。
英文摘要：
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.
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