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ORIGINAL RESEARCH article

Front. Cell Dev. Biol.
Sec. Molecular and Cellular Pathology
Volume 13 - 2025 | doi: 10.3389/fcell.2025.1508358
This article is part of the Research Topic Artificial Intelligence Applications in Chronic Ocular Diseases, Volume II View all 17 articles

A novel multi-scale and fine-grained network for large choroidal vessels segmentation in OCT

Provisionally accepted
Wei Huang Wei Huang 1Qifeng Yan Qifeng Yan 2*Lei Mou Lei Mou 2Yitian Zhao Yitian Zhao 2Wei Chen Wei Chen 1,3,4*
  • 1 School of Biomedical Engineering, Hainan University, Haikou, Hainan Province, China
  • 2 Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences (CAS), Ningbo, Zhejiang Province, China
  • 3 Ningbo Eye Hospital, Ningbo, Zhejiang Province, China
  • 4 Affiliated Eye Hospital to Wenzhou Medical University, Wenzhou, Zhejiang Province, China

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

    Accurate segmentation of large choroidal vessels using optical coherence tomography (OCT) images enables unprecedented quantitative analysis to understand choroidal diseases. In this paper, we propose a novel multi-scale and fine-grained network called MFGNet. Since choroidal vessels are small targets, long-range dependencies need to be considered, therefore, we developed a two-branch fine-grained feature extraction module that can mix the long-range information extracted by TransFormer with the local information extracted by convolution in parallel, introducing information exchange between the two branches. To address the problem of low contrast and blurred boundaries of choroidal vessels in OCT images, we developed a large kernel and multi-scale attention module, which can improve the features of the target area through multi-scale convolution kernels, channel mixing and feature refinement. We quantitatively evaluated the MFGNet on 800 OCT images with large choroidal vessels manually annotated.The experimental results show that the proposed method has the best performance compared to the most advanced segmentation networks currently available. It is noteworthy that the large choroidal vessels were reconstructed in three dimensions (3D) based on the segmentation results and several 3D morphological parameters were calculated. The statistical analysis of these parameters revealed significant differences between the healthy control group and the high myopia group, thereby confirming the value of the proposed work in facilitating subsequent understanding of the disease and clinical decision-making.

    Keywords: Optical Coherence Tomography, Choroid, Segmentation algorithm, 3D Reconstruction, feature analysis

    Received: 09 Oct 2024; Accepted: 06 Jan 2025.

    Copyright: © 2025 Huang, Yan, Mou, Zhao and Chen. 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:
    Qifeng Yan, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences (CAS), Ningbo, 315201, Zhejiang Province, China
    Wei Chen, School of Biomedical Engineering, Hainan University, Haikou, Hainan 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.