Among the research efforts to segment the retinal vasculature from fundus
images, deep learning models consistently achieve superior performance.
However, this data-driven approach is very sensitive to domain shifts. For
fundus images, such data distribution changes can easily be caused by
variations in illumination conditions as well as the presence of
disease-related features such as hemorrhages and drusen. Since the source
domain may not include all possible types of pathological cases, a model that
can robustly recognize vessels on unseen domains is desirable but remains
elusive, despite many proposed segmentation networks of ever-increasing
complexity. In this work, we propose a contrastive variational auto-encoder
that can filter out irrelevant features and synthesize a latent image, named
deep angiogram, representing only the retinal vessels. Then segmentation can be
readily accomplished by thresholding the deep angiogram. The generalizability
of the synthetic network is improved by the contrastive loss that makes the
model less sensitive to variations of image contrast and noisy features.
Compared to baseline deep segmentation networks, our model achieves higher
segmentation performance via simple thresholding. Our experiments show that the
model can generate stable angiograms on different target domains, providing
excellent visualization of vessels and a non-invasive, safe alternative to
fluorescein angiography.

本文提出了一种对比变分自编码器，可以过滤掉无关特征并合成一个名为深层血管造影的潜在图像来代表视网膜血管，并且通过简单阈值处理实现了更高的分割性能。这种合成网络的普适性得到了提高，可以在不同的目标领域生成稳定的血管造影，提供了一种良好的可视化方法，是荧光素血管造影的一个无创、安全的替代方法。