Development and evaluation of a deep learning model for automatic segmentation of non-perfusion area in fundus fluorescein angiography

IF 8.6 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS Journal of Big Data Pub Date : 2024-09-12 DOI:10.1186/s40537-024-00968-9
Wei Feng, Bingjie Wang, Dan Song, Mengda Li, Anming Chen, Jing Wang, Siyong Lin, Yiran Zhao, Bin Wang, Zongyuan Ge, Shuyi Xu, Yuntao Hu
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Abstract

Diabetic retinopathy (DR) is the most prevalent cause of preventable vision loss worldwide, imposing a significant economic and medical burden on society today, of which early identification is the cornerstones of the management. The diagnosis and severity grading of DR rely on scales based on clinical visualized features, but lack detailed quantitative parameters. Retinal non-perfusion area (NPA) is a pathogenic characteristic of DR that symbolizes retinal hypoxia conditions, and was found to be intimately associated with disease progression, prognosis, and management. However, the practical value of NPA is constrained since it appears on fundus fluorescein angiography (FFA) as distributed, irregularly shaped, darker plaques that are challenging to measure manually. In this study, we propose a deep learning-based method, NPA-Net, for accurate and automatic segmentation of NPAs from FFA images acquired in clinical practice. NPA-Net uses the U-net structure as the basic backbone, which has an encoder-decoder model structure. To enhance the recognition performance of the model for NPA, we adaptively incorporate multi-scale features and contextual information in feature learning and design three modules: Adaptive Encoder Feature Fusion (AEFF) module, Multilayer Deep Supervised Loss, and Atrous Spatial Pyramid Pooling (ASPP) module, which enhance the recognition ability of the model for NPAs of different sizes from different perspectives. We conducted extensive experiments on a clinical dataset with 163 eyes with NPAs manually annotated by ophthalmologists, and NPA-Net achieved better segmentation performance compared to other existing methods with an area under the receiver operating characteristic curve (AUC) of 0.9752, accuracy of 0.9431, sensitivity of 0.8794, specificity of 0.9459, IOU of 0.3876 and Dice of 0.5686. This new automatic segmentation model is useful for identifying NPA in clinical practice, generating quantitative parameters that can be useful for further research as well as guiding DR detection, grading severity, treatment planning, and prognosis.

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开发和评估用于自动分割眼底荧光素血管造影非灌注区的深度学习模型
糖尿病视网膜病变(DR)是全球最常见的可预防性视力丧失的原因,给当今社会造成了巨大的经济和医疗负担,而早期识别是治疗的基础。DR 的诊断和严重程度分级依赖于基于临床可视化特征的量表,但缺乏详细的量化参数。视网膜非灌注区(NPA)是 DR 的致病特征,象征着视网膜缺氧状况,并被发现与疾病进展、预后和管理密切相关。然而,NPA 的实用价值受到限制,因为它在眼底荧光素血管造影(FFA)中表现为分布不均、形状不规则、颜色较深的斑块,人工测量难度很大。在本研究中,我们提出了一种基于深度学习的方法 NPA-Net,用于从临床实践中获取的 FFA 图像中准确、自动地分割 NPA。NPA-Net 以 U-net 结构为基本骨干,具有编码器-解码器模型结构。为了提高模型对 NPA 的识别性能,我们在特征学习中自适应地加入了多尺度特征和上下文信息,并设计了三个模块:自适应编码器特征融合(AEFF)模块、多层深度监督损失(Multilayer Deep Supervised Loss)模块和阿特鲁斯空间金字塔池化(ASPP)模块,从不同角度提高了模型对不同规模 NPA 的识别能力。与其他现有方法相比,NPA-Net 获得了更好的分割性能,接收者工作特征曲线下面积(AUC)为 0.9752,准确率为 0.9431,灵敏度为 0.8794,特异性为 0.9459,IOU 为 0.3876,Dice 为 0.5686。这一新的自动分割模型有助于在临床实践中识别 NPA,生成有助于进一步研究的定量参数,并指导 DR 检测、严重程度分级、治疗计划和预后。
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来源期刊
Journal of Big Data
Journal of Big Data Computer Science-Information Systems
CiteScore
17.80
自引率
3.70%
发文量
105
审稿时长
13 weeks
期刊介绍: The Journal of Big Data publishes high-quality, scholarly research papers, methodologies, and case studies covering a broad spectrum of topics, from big data analytics to data-intensive computing and all applications of big data research. It addresses challenges facing big data today and in the future, including data capture and storage, search, sharing, analytics, technologies, visualization, architectures, data mining, machine learning, cloud computing, distributed systems, and scalable storage. The journal serves as a seminal source of innovative material for academic researchers and practitioners alike.
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