# Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations

arXiv: https://arxiv.org/abs/2306.10941 · HTML: https://mlsanity.com/paper/2306.10941/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 15 of 15)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [BOHB: Robust and Efficient Hyperparameter Optimization at Scale](https://mlsanity.com/paper/1807.01774/) (2018)
- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model](https://mlsanity.com/paper/2007.05201/) (2020)
- [Dual Contrastive Learning for Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/2104.07689/) (2021)
- [CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging](https://mlsanity.com/paper/2010.07486/) (2020)
- [Instance-wise Hard Negative Example Generation for Contrastive Learning in Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2108.04547/) (2021)
- [Realistic Adversarial Data Augmentation for MR Image Segmentation](https://mlsanity.com/paper/2006.13322/) (2020)
- [Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis](https://mlsanity.com/paper/2204.06929/) (2022)
- [Pathwise Derivatives Beyond the Reparameterization Trick](https://mlsanity.com/paper/1806.01851/) (2018)
- [SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis](https://mlsanity.com/paper/1908.04346/) (2019)
- [Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs](https://mlsanity.com/paper/2207.11102/) (2022)
- [A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images](https://mlsanity.com/paper/2007.05324/) (2020)
- [An Attentive-based Generative Model for Medical Image Synthesis](https://mlsanity.com/paper/2306.01562/) (2023)

## Built on by (most cited first, 17 of 17)

- [Representing Topological Self-Similarity Using Fractal Feature Maps for Accurate Segmentation of Tubular Structures](https://mlsanity.com/paper/2407.14754/) (2024)
- [Topologically Faithful Multi-class Segmentation in Medical Images](https://mlsanity.com/paper/2403.11001/) (2024)
- [3D Vessel Graph Generation Using Denoising Diffusion](https://mlsanity.com/paper/2407.05842/) (2024)
- [Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images](https://mlsanity.com/paper/2403.07116/) (2024)
- [Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis](https://mlsanity.com/paper/2503.09808/) (2025)
- [X-GAN: A Generative AI-Powered Unsupervised Model for Main Vessel Segmentation of Glaucoma Screening](https://mlsanity.com/paper/2503.06743/) (2025)
- [A label-free and data-free training strategy for vasculature segmentation in serial sectioning OCT data](https://mlsanity.com/paper/2405.13757/) (2024)
- [MM-UNet: Morph Mamba U-shaped Convolutional Networks for Retinal Vessel Segmentation](https://mlsanity.com/paper/2511.02193/) (2025)
- [Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review](https://mlsanity.com/paper/2502.14935/) (2025)
- [StructDiff: Structure-aware Diffusion Model for 3D Fine-grained Medical Image Synthesis](https://mlsanity.com/paper/2503.09560/) (2025)
- [Synthetic Vasculature and Pathology Enhance Vision-Language Model Reasoning](https://mlsanity.com/paper/2512.11060/) (2025)
- [LGESynthNet: Controlled Scar Synthesis for Improved Scar Segmentation in Cardiac LGE-MRI Imaging](https://mlsanity.com/paper/2603.18356/) (2026)
- [Gaze into the Details: Locality-Sensitive Enhancement for OCTA Retinal Vessel Segmentation](https://mlsanity.com/paper/2605.20651/) (2026)
- [In Defense of OCTA: The Reconstruction-Utility Gap in OCT-to-OCTA Synthesis](https://mlsanity.com/paper/2608.15626/) (2026)
- [Temporally Consistent Graph Extraction and Matching for Longitudinal Angiographic Images](https://mlsanity.com/paper/2609.16889/) (2026)
- [ReG-SAM: Reference Graph-Driven SAM for 2D Foundational Vessel Segmentation](https://mlsanity.com/paper/2609.31160/) (2026)
- [Temporal Diffusion Bridge Model for Vessel Extraction Using Physics-informed Synthetic Data](https://mlsanity.com/paper/2610.14105/) (2026)

## Abstract (arXiv)

Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio. The use of modern, deep learning-based segmentation methods has been inhibited by a lack of large datasets with detailed annotations of the blood vessels. To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data. However, the previously proposed simulations fail to faithfully model the retinal vasculature and do not provide effective domain adaptation. Because of this, current methods are unable to fully segment the retinal vasculature, in particular the smallest capillaries. In this work, we present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We then introduce three contrast adaptation pipelines to decrease the domain gap between real and artificial images. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets that compare our method to traditional computer vision algorithms and supervised training using human annotations. Finally, we make our entire pipeline publicly available, including the source code, pretrained models, and a large dataset of synthetic OCTA images.