Modality-Invariant Coarse-to-Fine Retinal Image Registration

[2608.14829] Modality-Invariant Coarse-to-Fine Retinal Image Registration Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Electrical Engineering and Systems Science > Image and Video Processing arXiv:2608.14829 (eess) [Submitted on 14 Aug 2026] Title: Modality-Invariant Coarse-to-Fine Retinal Image Registration Authors: Bo Wen , Nehal Nailesh Mehta , Melanie Tran , Dirk-Uwe Bartsch , William Freeman , Truong Nguyen View a PDF of the paper titled Modality-Invariant Coarse-to-Fine Retinal Image Registration, by Bo Wen and Nehal Nailesh Mehta and Melanie Tran and Dirk-Uwe Bartsch and William Freeman and Truong Nguyen View PDF HTML (experimental) Abstract: Retinal image registration is essential for ophthalmic diagnosis, longitudinal disease monitoring, and multimodal retinal image analysis. Existing retinal registration methods are typically modality-dependent: they are designed or optimized either for a single imaging modality in mono-modal registration or for a fixed pair of modalities in cross-modal registration. This limits their flexibility and applicability in practical scenarios involving diverse retinal imaging modalities and different combinations of them. In this work, we propose a generalizable two-stage, modality-invariant framework for retinal image registration. First, we introduce a sparse feature-matching model driven by a universal retinal vessel segmentation to achieve robust coarse global alignment across modalities. Second, we develop a modality-invariant optical flow estimation network, termed MI-RAFT, to refine the alignment through dense local registration. Extensive experiments demonstrate that the proposed method can handle diverse combinations of commonly used retinal imaging modalities, exhibiting strong modality invariance while outperforming state-of-the-art modality-dependent registration methods. Comments: This paper is a submission to IEEE Transactions on Image Processing (TIP-40498-2026) Subjects: Image and Video Processing (eess.IV) ; Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2608.14829 [eess.IV] (or arXiv:2608.14829v1 [eess.IV] for this version) https://doi.org/10.48550/arXiv.2608.14829 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Bo Wen [ view email ] [v1] Fri, 14 Aug 2026 19:03:17 UTC (19,336 KB) Full-text links: Access Paper: View a PDF of the paper titled Modality-Invariant Coarse-to-Fine Retinal Image Registration, by Bo Wen and Nehal Nailesh Mehta and Melanie Tran and Dirk-Uwe Bartsch and William Freeman and Truong Nguyen View PDF HTML (experimental) TeX Source view license Current browse context: eess.IV < prev | next > new | recent | 2026-08 Change to browse by: cs cs.CV eess References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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