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ORIGINAL RESEARCH article

Front. Cell Dev. Biol.
Sec. Molecular and Cellular Pathology
Volume 12 - 2024 | doi: 10.3389/fcell.2024.1532228
This article is part of the Research Topic Artificial Intelligence Applications in Chronic Ocular Diseases, Volume II View all 14 articles

A Multi-Modal Multi-Branch Framework for Retinal Vessel Segmentation using Ultra-WideField fundus photographs

Provisionally accepted
Qihang Xie Qihang Xie 1,2Xuefei Li Xuefei Li 2Yuanyuan Li Yuanyuan Li 1,2Yitian Zhao Yitian Zhao 2Jiong Zhang Jiong Zhang 1,2*
  • 1 Cixi Biomedical Research Institute, Wenzhou Medical 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.

    Vessel segmentation in fundus photography has become a cornerstone technique for disease analysis. Within this field, Ultra-WideField (UWF) fundus images offer distinct advantages, including an expansive imaging range, detailed lesion data, and minimal adverse effects. However, the high resolution and low contrast inherent to UWF fundus images present significant challenges for accurate segmentation using deep learning methods, thereby complicating disease analysis in this context. To address these issues, this study introduces M3B-Net, a novel multi-modal, multi-branch framework that leverages fundus fluorescence angiography (FFA) images to improve retinal vessel segmentation in UWF fundus images. Specifically, M3B-Net tackles the low segmentation accuracy caused by the inherently low contrast of UWF fundus images. Additionally, we propose an enhanced UWF-based segmentation network in M3B-Net, specifically designed to improve the segmentation of fine retinal vessels. The segmentation network includes the Selective Fusion Module (SFM), which enhances feature extraction within the segmentation network by integrating features generated during the FFA imaging process. To further address the challenges of high-resolution UWF fundus images, we introduce a Local Perception Fusion Module (LPFM) to mitigate context loss during the segmentation cut-patch process. Complementing this, the Attention-Guided Upsampling Module (AUM) enhances segmentation performance through convolution operations guided by attention mechanisms. Extensive experimental evaluations demonstrate that our approach significantly outperforms existing state-of-the-art methods for UWF fundus image segmentation.

    Keywords: Ultra-widefield, Fundus fluorescence angiography, Retinal vessel segmentation, multimodal framework, Selective fusion

    Received: 21 Nov 2024; Accepted: 20 Dec 2024.

    Copyright: © 2024 Xie, Li, Li, 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: Jiong Zhang, Cixi Biomedical Research Institute, Wenzhou Medical University, 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.