# Deep Angiogram: Trivializing Retinal Vessel Segmentation

arXiv: https://arxiv.org/abs/2307.00245 · HTML: https://mlsanity.com/paper/2307.00245/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 1 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [Unified cross-modality feature disentangler for unsupervised multi-domain MRI abdomen organs segmentation](https://mlsanity.com/paper/2007.09669/) (2020)
- [LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation](https://mlsanity.com/paper/2107.04282/) (2021)

## Built on by (most cited first, 1 of 1)

- [Novel OCT mosaicking pipeline with Feature- and Pixel-based registration](https://mlsanity.com/paper/2311.13052/) (2023)

## Abstract (arXiv)

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.