# Multimodal Transfer Learning-based Approaches for Retinal Vascular Segmentation

arXiv: https://arxiv.org/abs/2012.10160 · HTML: https://mlsanity.com/paper/2012.10160/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2020.

## Built on by (most cited first, 8 of 8)

- [Self-supervised learning methods and applications in medical imaging analysis: A survey](https://mlsanity.com/paper/2109.08685/) (2021)
- [MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis](https://mlsanity.com/paper/2506.08900/) (2025)
- [Simultaneous segmentation and classification of the retinal arteries and veins from color fundus images](https://mlsanity.com/paper/2209.09582/) (2022)
- [Self-supervised learning via inter-modal reconstruction and feature projection networks for label-efficient 3D-to-2D segmentation](https://mlsanity.com/paper/2307.03008/) (2023)
- [RRWNet: Recursive Refinement Network for effective retinal artery/vein segmentation and classification](https://mlsanity.com/paper/2402.03166/) (2024)
- [The Quest for Early Detection of Retinal Disease: 3D CycleGAN-based Translation of Optical Coherence Tomography into Confocal Microscopy](https://mlsanity.com/paper/2408.04091/) (2024)
- [X-ray transferable polyrepresentation learning](https://mlsanity.com/paper/2507.06264/) (2025)
- [Task-Aligned Self-Supervised Learning for Medical Image Analysis: A Task-Oriented Review with Practical Design Guidelines](https://mlsanity.com/paper/2605.23995/) (2026)

## Abstract (arXiv)

In ophthalmology, the study of the retinal microcirculation is a key issue in the analysis of many ocular and systemic diseases, like hypertension or diabetes. This motivates the research on improving the retinal vasculature segmentation. Nowadays, Fully Convolutional Neural Networks (FCNs) usually represent the most successful approach to image segmentation. However, the success of these models is conditioned by an adequate selection and adaptation of the architectures and techniques used, as well as the availability of a large amount of annotated data. These two issues become specially relevant when applying FCNs to medical image segmentation as, first, the existent models are usually adjusted from broad domain applications over photographic images, and second, the amount of annotated data is usually scarcer. In this work, we present multimodal transfer learning-based approaches for retinal vascular segmentation, performing a comparative study of recent FCN architectures. In particular, to overcome the annotated data scarcity, we propose the novel application of self-supervised network pretraining that takes advantage of existent unlabelled multimodal data. The results demonstrate that the self-supervised pretrained networks obtain significantly better vascular masks with less training in the target task, independently of the network architecture, and that some FCN architecture advances motivated for broad domain applications do not translate into significant improvements over the vasculature segmentation task.