  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.
