# Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs

arXiv: https://arxiv.org/abs/2207.11102 · HTML: https://mlsanity.com/paper/2207.11102/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 22 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Road Extraction by Deep Residual U-Net](https://mlsanity.com/paper/1711.10684/) (2017)
- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [CE-Net: Context Encoder Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/1903.02740/) (2019)
- [ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model](https://mlsanity.com/paper/2007.05201/) (2020)
- [Rethinking Ultrasound Augmentation: A Physics-Inspired Approach](https://mlsanity.com/paper/2105.02188/) (2021)
- [A distance-based loss for smooth and continuous skin layer segmentation in optoacoustic images](https://mlsanity.com/paper/2007.05324/) (2020)
- [Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic Data](https://mlsanity.com/paper/1908.07170/) (2019)

## Built on by (most cited first, 12 of 12)

- [Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations](https://mlsanity.com/paper/2306.10941/) (2023)
- [Advancing UWF-SLO Vessel Segmentation with Source-Free Active Domain Adaptation and a Novel Multi-Center Dataset](https://mlsanity.com/paper/2406.13645/) (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)
- [Neurovascular Segmentation in sOCT with Deep Learning and Synthetic Training Data](https://mlsanity.com/paper/2407.01419/) (2024)
- [Extremely weakly-supervised blood vessel segmentation with physiologically based synthesis and domain adaptation](https://mlsanity.com/paper/2305.17054/) (2023)
- [Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers](https://mlsanity.com/paper/2403.06601/) (2024)
- [A Hybrid Approach to Full-Scale Reconstruction of Renal Arterial Network](https://mlsanity.com/paper/2303.01837/) (2023)
- [Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias](https://mlsanity.com/paper/2210.16053/) (2022)
- [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)
- [Synthetic Vasculature and Pathology Enhance Vision-Language Model Reasoning](https://mlsanity.com/paper/2512.11060/) (2025)
- [Generative AI and Foundation Models in Medical Image](https://mlsanity.com/paper/2608.01686/) (2026)

## Abstract (arXiv)

Optical coherence tomography angiography (OCTA) can non-invasively image the eye's circulatory system. In order to reliably characterize the retinal vasculature, there is a need to automatically extract quantitative metrics from these images. The calculation of such biomarkers requires a precise semantic segmentation of the blood vessels. However, deep-learning-based methods for segmentation mostly rely on supervised training with voxel-level annotations, which are costly to obtain. In this work, we present a pipeline to synthesize large amounts of realistic OCTA images with intrinsically matching ground truth labels; thereby obviating the need for manual annotation of training data. Our proposed method is based on two novel components: 1) a physiology-based simulation that models the various retinal vascular plexuses and 2) a suite of physics-based image augmentations that emulate the OCTA image acquisition process including typical artifacts. In extensive benchmarking experiments, we demonstrate the utility of our synthetic data by successfully training retinal vessel segmentation algorithms. Encouraged by our method's competitive quantitative and superior qualitative performance, we believe that it constitutes a versatile tool to advance the quantitative analysis of OCTA images.