Chetoui Mohamed, Akhloufi Moulay A
Perception, Robotics, and Intelligent Machines Lab (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1A 3E9, Canada.
Biomedicines. 2025 Jan 9;13(1):141. doi: 10.3390/biomedicines13010141.
Retinal blood vessel segmentation plays an important role in diagnosing retinal diseases such as diabetic retinopathy, glaucoma, and hypertensive retinopathy. Accurate segmentation of blood vessels in retinal images presents a challenging task due to noise, low contrast, and the complex morphology of blood vessel structures. In this study, we propose a novel ensemble learning framework combining four deep learning architectures: U-Net, ResNet50, U-Net with a ResNet50 backbone, and U-Net with a transformer block. Each architecture is customized to enhance feature extraction and segmentation performance. The models are trained on the DRIVE and STARE datasets to improve the degree of generalization and evaluated using the performance metric accuracy, F1-Score, sensitivity, specificity, and AUC. The ensemble meta-model integrates predictions from these architectures using a stacking approach, achieving state-of-the-art performance with an accuracy of 0.9778, an AUC of 0.9912, and an F1-Score of 0.8231. These results demonstrate the performance of the proposed technique in identifying thin retinal blood vessels. A comparative analysis using qualitative and quantitative results with individual models highlights the robustness of the ensemble framework, especially under conditions of noise and poor visibility.
视网膜血管分割在诊断糖尿病视网膜病变、青光眼和高血压性视网膜病变等视网膜疾病中起着重要作用。由于噪声、低对比度以及血管结构的复杂形态，准确分割视网膜图像中的血管是一项具有挑战性的任务。在本研究中，我们提出了一种新颖的集成学习框架，该框架结合了四种深度学习架构：U-Net、ResNet50、带有ResNet50主干的U-Net以及带有Transformer模块的U-Net。每种架构都经过定制，以增强特征提取和分割性能。这些模型在DRIVE和STARE数据集上进行训练，以提高泛化程度，并使用性能指标准确率、F1分数、灵敏度、特异性和AUC进行评估。集成元模型使用堆叠方法整合这些架构的预测结果，实现了最先进的性能，准确率为0.9778，AUC为0.9912，F1分数为0.8231。这些结果证明了所提出技术在识别视网膜细血管方面的性能。使用单个模型的定性和定量结果进行的对比分析突出了集成框架的稳健性，尤其是在噪声和能见度差的条件下。
