# TPOT: Topology Preserving Optimal Transport in Retinal Fundus Image Enhancement

arXiv: https://arxiv.org/abs/2411.01403 · HTML: https://mlsanity.com/paper/2411.01403/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 3 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [Topology-Preserving Deep Image Segmentation](https://mlsanity.com/paper/1906.05404/) (2019)
- [Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces](https://mlsanity.com/paper/1907.05345/) (2019)
- [Optimal Transport for Unsupervised Denoising Learning](https://mlsanity.com/paper/2108.02574/) (2021)
- [Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels](https://mlsanity.com/paper/2103.03451/) (2021)
- [OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing](https://mlsanity.com/paper/2302.03003/) (2023)
- [Optimal Transport Guided Unsupervised Learning for Enhancing low-quality Retinal Images](https://mlsanity.com/paper/2302.02991/) (2023)
- [CUNSB-RFIE: Context-aware Unpaired Neural Schr"odinger Bridge in Retinal Fundus Image Enhancement](https://mlsanity.com/paper/2409.10966/) (2024)
- [nnMobileNet: Rethinking CNN for Retinopathy Research](https://mlsanity.com/paper/2306.01289/) (2023)
- [Context-Aware Optimal Transport Learning for Retinal Fundus Image Enhancement](https://mlsanity.com/paper/2409.07862/) (2024)

## Built on by (most cited first, 4 of 4)

- [nnMobileNet++: Towards Efficient Hybrid Networks for Retinal Image Analysis](https://mlsanity.com/paper/2512.01273/) (2025)
- [OphIn-500K: Curating Web-Scale Visual Instructions for Scaling Ophthalmic Multimodal Large Language Models](https://mlsanity.com/paper/2605.27916/) (2026)
- [SGW-GAN: Sliced Gromov-Wasserstein Guided GANs for Retinal Fundus Image Enhancement](https://mlsanity.com/paper/2601.13417/) (2026)
- [Bridging Restoration and Diagnosis: A Comprehensive Benchmark for Retinal Fundus Enhancement](https://mlsanity.com/paper/2604.03806/) (2026)

## Abstract (arXiv)

Retinal fundus photography enhancement is important for diagnosing and monitoring retinal diseases. However, early approaches to retinal image enhancement, such as those based on Generative Adversarial Networks (GANs), often struggle to preserve the complex topological information of blood vessels, resulting in spurious or missing vessel structures. The persistence diagram, which captures topological features based on the persistence of topological structures under different filtrations, provides a promising way to represent the structure information. In this work, we propose a topology-preserving training paradigm that regularizes blood vessel structures by minimizing the differences of persistence diagrams. We call the resulting framework Topology Preserving Optimal Transport (TPOT). Experimental results on a large-scale dataset demonstrate the superiority of the proposed method compared to several state-of-the-art supervised and unsupervised techniques, both in terms of image quality and performance in the downstream blood vessel segmentation task. The code is available at https://github.com/Retinal-Research/TPOT.