CcNet: A cross-connected convolutional network for segmenting retinal vessels using multi-scale features
CcNet: A cross-connected convolutional network for segmenting retinal vessels using multi-scale features
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CcNet：使用多尺度特征分割视网膜血管的交叉连接卷积网络
DOI：
10.1016/j.neucom.2018.10.098
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发表时间：
2020-06-07
期刊：
影响因子：
6
通讯作者：
Tian, Qi
中科院分区：
文献类型：
--
作者：
Feng, Shouting;Zhuo, Zhongshuo;Tian, Qi
视网膜血管分割（RVS）有助于糖尿病视网膜病变的诊断，糖尿病视网膜病变可导致视力障碍甚至失明。自动RVS在分割精度、鲁棒性、分割速度等方面存在着一些问题，阻碍了其应用.在本文中，我们提出了一个交叉连接的卷积神经网络（CcNet）的视网膜血管树的自动分割。在CcNet中，卷积层提取特征并根据这些学习的特征预测像素类。CcNet直接使用全绿色通道图像进行训练和测试。主路径和次路径之间的交叉连接融合了多层次特征。在两个公开数据集（DRIVE：Sn = 0.7625，Acc = 0.9528; STARE：Sn = 0.7709，Acc = 0.9633）上的实验结果高于大多数最先进的方法。在交叉训练阶段，CcNte在DRIVE和STARE上的精度波动（Delta Accs）分别为0.0042和0.007，与已发表的方法相比相对较小。此外，我们的算法具有更快的计算速度（0.063秒）比那些列出的算法使用GPU（图形处理单元）。这些结果表明，我们的算法具有潜在的实际应用中，由于有前途的分割性能，包括先进的特异性，准确性，鲁棒性和快速的处理速度。(c)2019 Elsevier B. V.版权所有。
Retinal vessel segmentation (RVS) helps the diagnosis of diabetic retinopathy, which can cause visual impairment and even blindness. Some problems are hindering the application of automatic RVS, including accuracy, robustness and segmentation speed. In this paper, we propose a cross-connected convolutional neural network (CcNet) for the automatic segmentation of retinal vessel trees. In the CcNet, convolutional layers extract the features and predict the pixel classes according to those learned features. The CcNet is trained and tested with full green channel images directly. The cross connections between primary path and secondary path fuse the multi-level features. The experimental results on two publicly available datasets (DRIVE: Sn = 0.7625, Acc = 0.9528; STARE: Sn = 0.7709, Acc = 0.9633) are higher than those of most state-of-the-art methods. In the cross-training phase, CcNte's accuracy fluctuations (Delta Accs) on DRIVE and STARE are 0.0042 and 0.007, respectively, which are relatively small compared with those of published methods. In addition, our algorithm has faster computing speed (0.063 s) than those listed algorithms using a GPU (graphics processing unit). These results reveal that our algorithm has potential in practical applications due to promising segmentation performances including advanced specificity, accuracy, robustness and fast processing speed. (c) 2019 Elsevier B.V. All rights reserved.