用拓扑保持的最优传输方法增强眼底图像，让血管结构更真实。
TPOT: Topology Preserving Optimal Transport in Retinal Fundus Image Enhancement
- 基于持久性图对比血管拓扑结构，用最优传输正则化训练
- 在大规模数据集上提升图像质量与血管分割准确率
- 适合眼科影像增强、医学图像生成等场景
眼底照相增强对视网膜疾病诊断和监测至关重要。早期基于生成对抗网络（GAN）的方法常难以保留血管的复杂拓扑结构，导致出现虚假或缺失的血管。持久性图能基于不同滤波下拓扑结构的持续性捕捉结构特征，为此我们提出一种拓扑保持训练范式，通过最小化持久性图差异来正则化血管结构，构建出拓扑保持最优传输（TPOT）框架。在大规模数据集上的实验表明，该方法在图像质量及下游血管分割任务中均优于多个先进监督与无监督技术。代码已开源：https://github.com/Retinal-Research/TPOT。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。