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。

深血管造影：简化视网膜血管分割

Deep Angiogram: Trivializing Retinal Vessel Segmentation

In Fluorescein Angiography (FA), an exogenous dye is injected in the
bloodstream to image the vascular structure of the retina. The injected dye can
cause adverse reactions such as nausea, vomiting, anaphylactic shock, and even
death. In contrast, color fundus imaging is a non-invasive technique used for
photographing the retina but does not have sufficient fidelity for capturing
its vascular structure. The only non-invasive method for capturing retinal
vasculature is optical coherence tomography-angiography (OCTA). However, OCTA
equipment is quite expensive, and stable imaging is limited to small areas on
the retina. In this paper, we propose a novel conditional generative
adversarial network (GAN) capable of simultaneously synthesizing FA images from
fundus photographs while predicting retinal degeneration. The proposed system
has the benefit of addressing the problem of imaging retinal vasculature in a
non-invasive manner as well as predicting the existence of retinal
abnormalities. We use a semi-supervised approach to train our GAN using
multiple weighted losses on different modalities of data. Our experiments
validate that the proposed architecture exceeds recent state-of-the-art
generative networks for fundus-to-angiography synthesis. Moreover, our vision
transformer-based discriminators generalize quite well on out-of-distribution
data sets for retinal disease prediction.

本文提出了一种新的条件生成对抗网络（GAN），能够同时从眼底照片中合成荧光素血管造影图像，并预测视网膜退化，从而解决了以非侵 asive 的方式对眼底血管成像以及预测视网膜异常的问题。