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SMFDNet: spatial and multi-frequency domain network for OCT angiography retinal vessel segmentation

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Abstract

Optical coherence tomography angiography (OCTA) is a non-invasive imaging technique, and automatic segmentation of retinal vessels is crucial for understanding ocular diseases and making informed clinical decisions. However, the automatic segmentation of retinal vessels in OCTA images is particularly challenging due to several inherent issues. Retinal vessels often exhibit low contrast against the surrounding tissue, making it difficult to distinguish them clearly. Additionally, the complex and irregular branching structures of retinal vessels, along with the presence of noise and artefacts in OCTA images, further complicate the segmentation task. To address these challenges, we propose a novel method, the spatial and multi-frequency domain-based segmentation network (SMFDNet), specifically designed for vessel segmentation in OCTA fundus images. This network effectively combines spatial and multi-frequency domain features to enhance the segmentation accuracy of retinal vessels. To demonstrate the superiority of our proposed network, we conduct experiments on the Retinal Vessels Images in OCTA (REVIO), Retinal OCT-Angiography Vessel Segmentation (ROSE) and Optical Coherence Tomography Angiography-500 (OCTA-500) datasets. The extensive experimental results show that our approach consistently outperforms state-of-the-art methods, particularly in handling low contrast and complex vessel structures. The codes are available at https://kyanbis.github.io/SMFDNet/.

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Data Availability

No datasets were generated or analysed during the current study.

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Acknowledgements

The authors would like to thank the editors and anonymous reviewers for their constructive comments and suggestions.

Funding

This work was supported by the Natural Science Foundation of Shandong Province (No. ZR2020MF105), Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology (No. 2020B121201010), the Natural National Science Foundation of China (Nos. 62175156 and 61675134) and Qufu Normal University Foundation for High Level Research (No. 116-607001).

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Authors and Affiliations

  1. School of Computer Science, Qufu Normal University, Rizhao, China

    Sien Li, Fei Ma, Jing Meng, Yanfei Guo, Hongjuan Liu & Ronghua Cheng

  2. Ultrasound Medicine Department Qufu People’s Hospital, Qufu, China

    Fen Yan

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Contributions

Conception and design were performed by S.L. and F.M.; (II) administrative support was provided by F. M. and J.M.; (III) provision of study materials or patients was distributed by J.M. and Y.F.; (IV) collection and assembly of data were carried out by F. M.; (V) data analysis and interpretation were conducted by S.L., Y.G., H.L. and R.C.; (VI) manuscript writing was done by all authors; (VII) final approval of manuscript was approved by all authors.

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Correspondence to Fei Ma.

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Li, S., Ma, F., Yan, F. et al. SMFDNet: spatial and multi-frequency domain network for OCT angiography retinal vessel segmentation. J Supercomput 81, 535 (2025). https://doi.org/10.1007/s11227-025-06985-6

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