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ORIGINAL RESEARCH article

Front. Mol. Biosci.

Sec. Molecular Diagnostics and Therapeutics

Volume 12 - 2025 | doi: 10.3389/fmolb.2025.1562608

This article is part of the Research TopicApplication of Deep Learning in Biomedical Image ProcessingView all 8 articles

RetinalVasNet: A Deep Learning Approach for Robust Retinal Microvasculature Detection

Provisionally accepted
Zhaomin  YaoZhaomin Yao1Cengcong  XingCengcong Xing2Gancheng  ZhuGancheng Zhu3Weiming  XieWeiming Xie1Zhiguo  WangZhiguo Wang4*Zhang  GuoxuZhang Guoxu1,4
  • 1Northeastern University, Shenyang, Liaoning Province, China
  • 2East China Normal University, Shanghai, Shanghai Municipality, China
  • 3Zhejiang University, Hangzhou, Zhejiang Province, China
  • 4Northern Theater Command General Hospital, Shenyang, China

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

The retinal microvasculature has been definitively linked to a variety of diseases, such as ophthalmological, cardiovascular, and other medical conditions. Precisely identifying the retinal microvasculature is crucial for early detection and monitoring of these diseases. While the majority of existing neural network-based research has primarily focused on utilizing the green channel of fundus images for vessel segmentation, it is important to acknowledge the potential value of other channels in this process. As a result, this study introduces RetinalVasNet, a new method aimed at enhancing the accuracy and effectiveness of retinal vascular segmentation by implementing a sophisticated neural network architecture and incorporating multi-channel fundus images. Our experimental results demonstrate that RetinalVasNet outperforms previous research in most performance metrics. Additionally, the findings suggest that each channel provides unique contributions to the vascular segmentation process, emphasizing the importance of incorporating multiple channels for accurate and comprehensive segmentation.

Keywords: Channel fusion, Fundus images, Retinal microvasculature, RetinalVasNet, Vessel segmentation

Received: 17 Jan 2025; Accepted: 31 Jul 2025.

Copyright: © 2025 Yao, Xing, Zhu, Xie, Wang and Guoxu. 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: Zhiguo Wang, Northern Theater Command General Hospital, Shenyang, China

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