National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, College of Artificial Intelligence, Nankai University, Tianjin, 300000, China.
Ophthalmology, Tianjin Huanhu Hospital, Tianjin, 300000, China.
Interdiscip Sci. 2024 Dec;16(4):926-935. doi: 10.1007/s12539-024-00650-x. Epub 2024 Sep 2.
As a common disease, cardiovascular and cerebrovascular diseases pose a great harm threat to human wellness. Even using advanced and comprehensive treatment methods, there is still a high mortality rate. Arteriosclerosis, as an important factor reflecting the severity of cardiovascular and cerebrovascular diseases, is imperative to detect the arteriosclerotic retinopathy. However, the detection of arteriosclerosis retinopathy requires expensive and time-consuming manual evaluation, while end-to-end deep learning detection methods also need interpretable design to high light task-related features. Considering the importance of automatic arteriosclerotic retinopathy grading, we propose a segmentation and classification interaction network (SCINet). We propose a segmentation and classification interaction architecture for grading arteriosclerotic retinopathy. After IterNet is used to segment retinal vessel from original fundus images, the backbone feature extractor roughly extracts features from the segmented and original fundus arteriosclerosis images and further enhances them through the vessel aware module. The last classifier module generates fundus arteriosclerosis grading results. Specifically, the vessel aware module is designed to highlight the important areal vessel features segmented from original images by attention mechanism, thereby achieving information interaction. The attention mechanism selectively learns the vessel features of segmentation region information under the proposed interactive architecture, which leads to reweighting the extracted features and enhances significant feature information. Extensive experiments have confirmed the effect of our model. SCINet has the best performance on the task of arteriosclerotic retinopathy grading. Additionally, the CNN method is scalable to similar tasks by incorporating segmented images as auxiliary information.
作为一种常见疾病，心脑血管疾病对人类健康构成了巨大的危害威胁。即使采用先进和全面的治疗方法，仍然存在高死亡率。动脉粥样硬化作为反映心脑血管疾病严重程度的重要因素，对检测动脉粥样硬化性视网膜病变至关重要。然而，动脉粥样硬化性视网膜病变的检测需要昂贵且耗时的人工评估，而端到端的深度学习检测方法也需要可解释的设计来突出与任务相关的特征。考虑到自动动脉粥样硬化性视网膜病变分级的重要性，我们提出了分割与分类交互网络（SCINet）。我们提出了一种用于分级动脉粥样硬化性视网膜病变的分割与分类交互架构。在使用 IterNet 从原始眼底图像中分割视网膜血管后，骨干特征提取器从分割后的和原始眼底动脉粥样硬化图像中粗略提取特征，并通过血管感知模块进一步增强。最后，分类器模块生成眼底动脉粥样硬化分级结果。具体来说，血管感知模块通过注意力机制设计来突出从原始图像中分割出的重要区域血管特征，从而实现信息交互。注意力机制在提出的交互架构下选择性地学习分割区域信息的血管特征，从而对提取的特征进行重新加权，并增强显著特征信息。广泛的实验证实了我们模型的效果。SCINet 在动脉粥样硬化性视网膜病变分级任务上具有最佳性能。此外，通过将分割图像作为辅助信息，CNN 方法可以扩展到类似任务。
