Liu Qingyou, Zhou Fen, Shen Jianxin, Xu Jianguo, Wan Cheng, Xu Xiangzhong, Yan Zhipeng, Yao Jin
College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China.
Front Cell Dev Biol. 2024 Oct 3;12:1477819. doi: 10.3389/fcell.2024.1477819. eCollection 2024.
Fundus vessel segmentation is vital for diagnosing ophthalmic diseases like central serous chorioretinopathy (CSC), diabetic retinopathy, and glaucoma. Accurate segmentation provides crucial vessel morphology details, aiding the early detection and intervention of ophthalmic diseases. However, current algorithms struggle with fine vessel segmentation and maintaining sensitivity in complex regions. Challenges also stem from imaging variability and poor generalization across multimodal datasets, highlighting the need for more advanced algorithms in clinical practice.
This paper aims to explore a new vessel segmentation method to alleviate the above problems. We propose a fundus vessel segmentation model based on a combination of double skip connections, deep supervision, and TransUNet, namely DS2TUNet. Initially, the original fundus images are improved through grayscale conversion, normalization, histogram equalization, gamma correction, and other preprocessing techniques. Subsequently, by utilizing the U-Net architecture, the preprocessed fundus images are segmented to obtain the final vessel information. Specifically, the encoder firstly incorporates the ResNetV1 downsampling, dilated convolution downsampling, and Transformer to capture both local and global features, which upgrades its vessel feature extraction ability. Then, the decoder introduces the double skip connections to facilitate upsampling and refine segmentation outcomes. Finally, the deep supervision module introduces multiple upsampling vessel features from the decoder into the loss function, so that the model can learn vessel feature representations more effectively and alleviate gradient vanishing during the training phase.
Extensive experiments on publicly available multimodal fundus datasets such as DRIVE, CHASE_DB1, and ROSE-1 demonstrate that the DS2TUNet model attains F1-scores of 0.8195, 0.8362, and 0.8425, with Accuracy of 0.9664, 0.9741, and 0.9557, Sensitivity of 0.8071, 0.8101, and 0.8586, and Specificity of 0.9823, 0.9869, and 0.9713, respectively. Additionally, the model also exhibits excellent test performance on the clinical fundus dataset CSC, with F1-score of 0.7757, Accuracy of 0.9688, Sensitivity of 0.8141, and Specificity of 0.9801 based on the weight trained on the CHASE_DB1 dataset. These results comprehensively validate that the proposed method obtains good performance in fundus vessel segmentation, thereby aiding clinicians in the further diagnosis and treatment of fundus diseases in terms of effectiveness and feasibility.
眼底血管分割对于诊断诸如中心性浆液性脉络膜视网膜病变（CSC）、糖尿病视网膜病变和青光眼等眼科疾病至关重要。准确的分割提供了关键的血管形态细节，有助于眼科疾病的早期检测和干预。然而，当前的算法在精细血管分割以及在复杂区域保持敏感性方面存在困难。挑战还源于成像的变异性以及跨多模态数据集的泛化性较差，这凸显了临床实践中对更先进算法的需求。
本文旨在探索一种新的血管分割方法以缓解上述问题。我们提出了一种基于双跳连接、深度监督和TransUNet相结合的眼底血管分割模型，即DS2TUNet。首先，通过灰度转换、归一化、直方图均衡化、伽马校正等预处理技术对原始眼底图像进行改进。随后，利用U-Net架构对预处理后的眼底图像进行分割以获得最终的血管信息。具体而言，编码器首先结合ResNetV1下采样、扩张卷积下采样和Transformer来捕获局部和全局特征，从而提升其血管特征提取能力。然后，解码器引入双跳连接以促进上采样并细化分割结果。最后，深度监督模块将来自解码器的多个上采样血管特征引入损失函数，使得模型能够更有效地学习血管特征表示并缓解训练阶段的梯度消失问题。
在诸如DRIVE、CHASE_DB1和ROSE-1等公开可用的多模态眼底数据集上进行的大量实验表明，DS2TUNet模型分别获得了0.8195、0.8362和0.8425的F1分数，准确率分别为0.9664、0.9741和0.9557，敏感性分别为0.8071、0.8101和0.8586，特异性分别为0.9823、0.9869和0.9713。此外，基于在CHASE_DB1数据集上训练的权重，该模型在临床眼底数据集CSC上也表现出出色的测试性能，F1分数为0.7757，准确率为0.9688，敏感性为0.8141，特异性为0.9801。这些结果全面验证了所提出的方法在眼底血管分割中取得了良好的性能，从而在有效性和可行性方面辅助临床医生对眼底疾病进行进一步的诊断和治疗。
