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ORIGINAL RESEARCH article

Front. Med.
Sec. Ophthalmology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1542737
This article is part of the Research Topic Imaging in the Diagnosis and Treatment of Eye Diseases View all 12 articles

Dual-stream disentangled model for microvascular extraction in five datasets from multiple OCTA instruments

Provisionally accepted
Xiaoyang Hu Xiaoyang Hu 1,2Jinkui Hao Jinkui Hao 2Yitian Zhao Yitian Zhao 2*Jiong Zhang Jiong Zhang 2*
  • 1 Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, Zhejiang Province, China
  • 2 Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences,, Ningbo, Zhejiang Province, China

The final, formatted version of the article will be published soon.

    Optical Coherence Tomography Angiography (OCTA) is a cutting-edge imaging technique that captures retinal capillaries at micrometer resolution using optical instrument. Accurate segmentation of retinal vasculature is essential for eye-related diseases measurement and diagnosis. However, noise and artifacts from different imaging instruments can interfere with segmentation, and most existing deep learning models struggle with segmenting small vessels and capturing low-dimensional structural information. These challenges typically results in less precise segmentation performance. Therefore, we propose a novel and robust Dual-stream Disentangled Network (D2Net) for retinal OCTA microvascular segmentation. Specifically, the D2Net includes a dual-stream encoder that separately learns image artifacts and latent vascular features. By introducing vascular structure as a prior constraint and constructing auxiliary information, the network achieves disentangled representation learning, effectively minimizing the interference of noise and artifacts. The introduced vascular structure prior includes lowdimensional neighborhood energy from the Distance Correlation Energy (DCE) module, which helps to better perceive the structural information of continuous vessels. To precisely evaluate our method on small vessels, we delicately establish OCTA microvascular labels by performing comprehensive and detailed annotations on the FOCA dataset, which includes data collected from different instruments, and evaluated the proposed method. Experimental results demonstrate that the proposed D2Net effectively mitigates the challenges of microvasculature region recognition caused by noise and artifacts. The method achieves more refined segmentation performance.In addition, we validated the performance of D2Net on four OCTA datasets (OCTA-500, ROSE-O, ROSE-Z, and ROSE-H) acquired using different instruments, demonstrating its robustness and generalization capabilities in retinal vessel segmentation compared to other state-of-the-art methods.

    Keywords: OCTA, cross-instruments, microvascular segmentation, vessel measurements, Disentanglement

    Received: 10 Dec 2024; Accepted: 08 Jan 2025.

    Copyright: © 2025 Hu, Hao, Zhao and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Yitian Zhao, Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences,, Ningbo, Zhejiang Province, China
    Jiong Zhang, Laboratory of Advanced Theranostic Materials and Technology, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences,, Ningbo, Zhejiang Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.