Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes. Optimal retinal image quality is mandated for accurate medical diagnoses and automated analyses. Herein, we leveraged the Optimal Transport (OT) theory to propose an unpaired image-to-image translation scheme for mapping low-quality retinal CFPs to high-quality counterparts. Furthermore, to improve the flexibility, robustness, and applicability of our image enhancement pipeline in the clinical practice, we generalized a state-of-the-art model-based image reconstruction method, regularization by denoising, by plugging in priors learned by our OT-guided image-to-image translation network. We named it as regularization by enhancing (RE). We validated the integrated framework, OTRE, on three publicly available retinal image datasets by assessing the quality after enhancement and their performance on various downstream tasks, including diabetic retinopathy grading, vessel segmentation, and diabetic lesion segmentation. The experimental results demonstrated the superiority of our proposed framework over some state-of-the-art unsupervised competitors and a state-of-the-art supervised method.
翻译:由于不要求子宫颈放大的优点,非色质视网膜颜色摄影(CFP)可以广泛使用,但由于操作者、系统不完善或与病人有关的原因,不要求子宫放大的优点,很容易导致质量低劣。最佳视网膜图像质量被授权用于准确的医疗诊断和自动分析。在这里,我们利用最佳交通(OT)理论,向高质量的对应方提出一个未受重视的图像到图像翻译计划,用于绘制低质量视网膜成像板。此外,为了提高临床实践中我们图像增强管道的灵活性、稳健性和可适用性,我们推广了一种基于最新设计模型的图像重建方法,通过调换,通过插入我们OT指导的图像到图像翻译网络所学的先前知识,实现正规化。我们称之为通过强化(RE)实现正规化。我们通过评估各种下游任务的质量及其绩效,包括分解式的再分析、容器级分析、容器升级和诊断性升级后,我们所展示的实验性优劣性等级分析框架。