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| 基于U型卷积网络的视网膜血管分割方法 |
| Retinal vessel segmentation based on U-shaped convolutional network |
| 投稿时间：2020-07-10 |
| DOI：10.3969/j.issn.1005-5630.2021.02.004 |
| 中文关键词: 视网膜血管 U型卷积网络 编解码 通道注意力模块 |
| 英文关键词:retinal vessel segmentation U-shaped convolutional network encoder-decode channel attention module |
| 基金项目:上海市人工智能专项（2019-RGZN-01077） |
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| 摘要点击次数: 2583 |
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| 中文摘要: |
| 视网膜血管的结构信息对眼科疾病的诊断具有重要的指导意义，对视网膜血管图像进行高效正确的分割成为临床的迫切需求。为此，提出了一种U型卷积网络，实现了更高效的自动化视网膜血管分割。骨干网络基于经典的编解码架构，编码器采用预训练的残差模块充分提取每一层的特征，解码器通过转置卷积逐层进行上采样，增加了特征的复用性。网络在中间层引入ASPP(Atrous Spatial Pyramid Pooling)模块，提取不同尺度的视网膜血管特征。为了在类内预测上保持一致，在跳级层利用通道注意力模块对特征进行自适应细化，融合了不同层次的特征。在DRIVE数据集上的实验结果表明，与其他相关算法性能相比，该算法的敏感性、特异性、准确率均最高，模型泛化能力好，大大提高了视网膜血管分割的准确性。 |
| 英文摘要: |
| The structural information of retinal blood vessels has important guiding significance for the diagnosis of ophthalmic diseases. Efficient and correct segmentation of retinal vessel images has become an urgent clinical requirement. A U-shaped convolutional network is proposed to achieve more efficient automatic retinal vessel segmentation. The backbone network is based on classical encoder-decoder architecture. The encoder module uses the pre-trained residual module to fully extract the features of each layer, and the decoder module carries out the up-sampling layer by layer through the transposed convolution, which increases the multiplex ability of features. The network introduces the atrous spatial pyramid pooling (ASPP) block in the middle layer to extract retinal vessel characteristics of different scales. In order to keep consistency in the prediction within the class, the channel attention block is introduced in the skip connection layer to carry out the adaptive refinement of the features, and the features of different levels are fused. Experimental results on the DRIVE data set show that compared with the performance of other related algorithms, this algorithm has the highest sensitivity, specificity and accuracy, the best model generalization ability, and greatly improves the accuracy of retinal vessel segmentation. |
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