# Cross-modal Fundus Image Registration under Large FoV Disparity

arXiv: https://arxiv.org/abs/2512.12657 · HTML: https://mlsanity.com/paper/2512.12657/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 14 Dec 2025.

## Builds on (most cited first, 7 of 7)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching](https://mlsanity.com/paper/2207.07932/) (2022)
- [An Accurate and Efficient Neural Network for OCTA Vessel Segmentation and a New Dataset](https://mlsanity.com/paper/2309.09483/) (2023)
- [Multi-modal Retinal Image Registration Using a Keypoint-Based Vessel Structure Aligning Network](https://mlsanity.com/paper/2207.10506/) (2022)
- [Convolutional Prompting for Broad-Domain Retinal Vessel Segmentation](https://mlsanity.com/paper/2412.18089/) (2024)

## Abstract (arXiv)

Previous work on cross-modal fundus image registration (CMFIR) assumes small cross-modal Field-of-View (FoV) disparity. By contrast, this paper is targeted at a more challenging scenario with large FoV disparity, to which directly applying current methods fails. We propose Crop and Alignment for cross-modal fundus image Registration(CARe), a very simple yet effective method. Specifically, given an OCTA with smaller FoV as a source image and a wide-field color fundus photograph (wfCFP) as a target image, our Crop operation exploits the physiological structure of the retina to crop from the target image a sub-image with its FoV roughly aligned with that of the source. This operation allows us to re-purpose the previous small-FoV-disparity oriented methods for subsequent image registration. Moreover, we improve spatial transformation by a double-fitting based Alignment module that utilizes the classical RANSAC algorithm and polynomial-based coordinate fitting in a sequential manner. Extensive experiments on a newly developed test set of 60 OCTA-wfCFP pairs verify the viability of CARe for CMFIR.