Exploration of Digital Health Technologies

Method	Time per image (seconds)
RetinaRegNet	31.23
EyeLiner	4.92
GeoFormer	0.32

Supplementary materials

The supplementary materials for this article are available at: https://www.explorationpub.com/uploads/Article/file/101194_sup_1.pdf.

Declarations

Author contributions

TD: Conceptualization, Methodology, Data curation, Formal analysis, Project administration, Writing—original draft, Writing—review & editing, Visualization, Writing—review & editing, Funding acquisition. NS: Conceptualization, Investigation, Methodology, Formal analysis, Data curation, Visualization, Writing—original draft, Writing—review & editing. YWC: Supervision, Conceptualization, Methodology, Validation, Writing—review & editing, Funding acquisition. SA: Supervision, Writing—review & editing, Writing—original draft. KD: Investigation, Writing—original draft, Writing—review & editing. PG: Writing—review & editing. YTC: Writing–review & editing. AJ: Validation. NP: Supervision, Project administration, Validation, Writing—review & editing, Funding acquisition. All authors read and approved the submitted version.

Conflicts of interest

NP is a patent holder of PCT/EP2023/076614 filed by UCL Business. The authors declare that they have no other relevant conflicts of interest.

Ethical approval

This study utilised the FIRE dataset, which is publicly available, so it involves no new data collection or direct interaction with human participants. All data are fully anonymised. Therefore, ethical approval was not required.

Consent to participate

This study involved secondary analysis of publicly available, anonymised retinal image data and did not involve direct participation of human subjects. Accordingly, consent to participate was not required.

Consent to publication

No identifiable individual data are presented in this study. Therefore, consent to publication was not required.

Availability of data and materials

The FIRE dataset analysed during this study is publicly available at: https://projects.ics.forth.gr/cvrl/fire/ and was used in accordance with the terms of use. No additional datasets were generated or analysed. Code used for analysis is available from the corresponding author upon reasonable request.

Funding

The research was supported by a grant from the National Institute for Health Research (NIHR) Biomedical Research Centre (BRC) at MEH NHS Foundation Trust and UCL Institute of Ophthalmology (grant no. NIHR203322). N.P. is funded by an Artificial Intelligence in Health and Care Award (NIHR AI Award grant no. AI_AWARD02488). The Artificial Intelligence in Health and Care Award is part of the NHS AI Laboratory, which has made funding available to accelerate the testing and evaluation of artificial intelligence technologies that meet the aims set out in the NHS Long Term Plan. The NHS AI Laboratory is a joint unit of teams from the Department of Health and Social Care and NHS England, driving forward the digital transformation of health and social care (https://transform.england.nhs.uk/ai-lab/). N.P. is also funded by Sight Research UK (grant no. TRN004). N.P. and Y.W.C are also funded by Medical Research Foundation and Moorfields Eye Charity (grant no. MRF-JF-EH-23-122) and Fight for Sight (grant no. RESSGA250). T.D. and N.P. are also funded by NIHR i4i THRIVE (grant no. NIHR505133). N.P. was also previously funded by Retina UK as part of the UK IRD Consortium, Moorfields Eye Charity Career Development Award (grant no. R190031A), HDRUK (grant no. MC_PC_18036) and by a Translational Innovation grant awarded by the UCL Translational Research Office, which has seed funded this work. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright

Publisher’s note

Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.

References

Ramsey DJ, Sunness JS, Malviya P, Applegate C, Hager GD, Handa JT. Automated image alignment and segmentation to follow progression of geographic atrophy in age-related macular degeneration. Retina. 2014;34:1296–307. [DOI] [PubMed]

Hussain MA, Govindaiah A, Souied E, Smith RT, Bhuiyan A. Automated tracking and change detection for age-related macular degeneration progression using retinal fundus imaging. 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR); 2018 Jun 25–29; Kitakyushu, Japan. IEEE; pp. 394–8. [DOI]

Balakrishnan G, Zhao A, Sabuncu MR, Guttag J, Dalca AV. VoxelMorph: A Learning Framework for Deformable Medical Image Registration. IEEE Trans Med Imaging. 2019. [DOI] [PubMed]

Sokooti H, de Vos B, Berendsen F, Lelieveldt BPF, Išgum I, Staring M. Nonrigid Image Registration Using Multi-scale 3D Convolutional Neural Networks. In: Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins D, Duchesne S, editors. Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. MICCAI 2017. Cham: Springer; 2017. [DOI]

Zhang J, Wang Y, Dai J, Cavichini M, Bartsch DG, Freeman WR, et al. Two-Step Registration on Multi-Modal Retinal Images via Deep Neural Networks. IEEE Trans Image Process. 2022;31:823–38. [DOI] [PubMed] [PMC]

Zhang J, Wang Y, Bartsch DG, Freeman WR, Nguyen TQ, An C. Perspective Distortion Correction for Multi-Modal Registration between Ultra-Widefield and Narrow-Angle Retinal Images. Annu Int Conf IEEE Eng Med Biol Soc. 2021;2021:4086–91. [DOI] [PubMed] [PMC]

Zhang J, An C, Dai J, Amador M, Bartsch DU, Borooah S. Joint Vessel Segmentation and Deformable Registration on Multi-Modal Retinal Images Based on Style Transfer. 2019 IEEE International Conference on Image Processing (ICIP); 2019 Sep 22–25; Taipei, Taiwan. IEEE; 2019. pp. 839–43. [DOI]

Benvenuto GA, Colnago M, Casaca W. Unsupervised Deep Learning Network for Deformable Fundus Image Registration. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2022 May 23–27; Singapore, Singapore. IEEE; 2022. pp. 1281–5. [DOI]

Tian Y, Hu Y, Ma Y, Hao H, Mou L, Yang J, et al. Multi-scale U-net with Edge Guidance for Multimodal Retinal Image Deformable Registration. Annu Int Conf IEEE Eng Med Biol Soc. 2020;2020:1360–3. [DOI] [PubMed]

10.

Zou B, He Z, Zhao R, Zhu C, Liao W, Li S. Non-rigid retinal image registration using an unsupervised structure-driven regression network. Neurocomputing. 2020;404:14–25. [DOI]

11.

Liu J, Li X, Wei Q, Xu J, Ding D. Semi-Supervised Keypoint Detector and Descriptor for Retinal Image Matching. In: Avidan S, Brostow G, Cissé M, Farinella GM, Hassner T, editors. Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science. 2022. pp. 593–609. [DOI]

12.

Wang AQ, Yu EM, Dalca AV, Sabuncu MR. A robust and interpretable deep learning framework for multi-modal registration via keypoints. Med Image Anal. 2023;90:102962. [DOI] [PubMed] [PMC]

13.

Chen J, Frey EC, He Y, Segars WP, Li Y, Du Y. TransMorph: Transformer for unsupervised medical image registration. Med Image Anal. 2022;82:102615. [DOI] [PubMed] [PMC]

14.

Sun J, Shen Z, Wang Y, Bao H, Zhou X. LoFTR: Detector-Free Local Feature Matching with Transformers. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2021 Jun 20–25; Nashville, USA. IEEE; 2021. pp. 8918–27. [DOI]

15.

Ho J, Jain A, Abbeel P. Denoising Diffusion Probabilistic Models. Adv Neural Inf Process Syst. 2020.

16.

Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B. High-Resolution Image Synthesis with Latent Diffusion Models. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2022 Jun 18–24; New Orleans, USA. IEEE; 2022. pp. 10674–85. [DOI]

17.

Sivaraman VB, Imran M, Wei Q, Muralidharan P, Tamplin MR, Grumbach IM, et al. RetinaRegNet: A zero-shot approach for retinal image registration. Comput Biol Med. 2025;186:109645. [DOI] [PubMed] [PMC]

18.

Veturi YA, McNamara S, Kinder S, Clark CW, Thakuria U, Bearce B, et al. EyeLiner: A Deep Learning Pipeline for Longitudinal Image Registration Using Fundus Landmarks. Ophthalmol Sci. 2024;5:100664. [DOI] [PubMed] [PMC]

19.

Liu J, Li X. Geometrized Transformer for Self-Supervised Homography Estimation. Proceedings of the IEEE International Conference on Computer Vision. 2023 October 1–6; Paris, France. IEEE; 2023. pp. 9522–31. [DOI]

20.

Hernandez-Matas C, Zabulis X, Triantafyllou A, Anyfanti P, Douma S, Argyros AA. FIRE: Fundus Image Registration dataset. Model Artif Intell Ophthalmol. 2017;1:16–28. [DOI]

21.

Zhou Y, Wagner SK, Chia MA, Zhao A, Woodward-Court P, Xu M, et al. AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning Pipeline. Transl Vis Sci Technol. 2022;11:12. [DOI] [PubMed] [PMC]

22.

Cheng B, Schwing AG, Kirillov A. Per-Pixel Classification is Not All You Need for Semantic Segmentation. Adv Neural Inf Process Syst. 2021;22:17864–75.

23.

Detone D, Malisiewicz T, Rabinovich A. SuperPoint: Self-Supervised Interest Point Detection and Description. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 2018 Jun 18–22; Salt Lake City, USA. IEEE; 2018. pp. 337–49. [DOI]

24.

Lindenberger P, Sarlin PE, Pollefeys M. LightGlue: Local Feature Matching at Light Speed. 2023 IEEE/CVF International Conference on Computer Vision (ICCV); 2023 Oct 1–6; Paris, France. IEEE; 2023. pp. 17581–92. [DOI]

25.

Lin TY, Dollar P, Girshick R, He K, Hariharan B, Belongie S. Feature Pyramid Networks for Object Detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017 Jul 21–26; Honolulu, HI, USA. IEEE; 2017. pp. 936–44. [DOI]