Improved U-Net Network for Retinal Vascular Segmentation

doi:10.3778/j.issn.1673-9418.2107060

Abstract

Abstract: The morphological and structural information of retinal blood vessels can provide a basis for diagnosis of diabetes and hypertension. Most of the existing algorithms improve the segmentation accuracy by fusing the feature information of different stages of blood vessels, but they ignore the problem that the shallow information is diluted during the upsampling process after fusing, and the more times of upsampling, the more serious the dilution is. A boosting algorithm that incorporates probabilistic distributions of attention is proposed to solve the above problem. Based on the U-Net model, this paper firstly uses multiple serial boosting blocks to learn the shallow feature information of the vessels. Then, it introduces a probability distribution attention mechanism in the boosting blocks to change the probability distribution and improve the contribution of the shallow information in the final segmen-tation task. Finally, it uses deeply supervised asymmetric aggregation to supplement the gradient flow of each boosting block to avoid gradient disappearance. It is compared with different algorithms in recent years on the DRIVE and STARE datasets. The SP values of proposed algorithm are 0.9857 and 0.9917 respectively, which are 0.2% and 0.25% higher than the second one, both better than the latest algorithm results. The experiments demonstrate that the boosting algorithm can better improve the pre-vascular information, solve the information dilution problem, and improve the segmentation of retinal vessels.

Key words: retinal vessels, boosting algorithm, probability distribution attention, deeply supervised asymmetric aggregation

摘要： 视网膜血管的形态结构信息可以为糖尿病、高血压等疾病提供诊断依据。现有的大部分算法通过融合血管不同阶段的特征信息来提升分割精度，却忽略了融合后浅层信息在采样过程中容易被稀释，且上采样次数越多，稀释越严重的问题。为此提出了一种融合概率分布注意力的提升算法用以解决上述问题。该算法基于U-Net模型，首先利用多个串行提升块学习血管浅层的特征信息，然后在提升块中引入概率分布注意力机制用来改变浅层信息的概率分布，提升浅层信息在最终分割任务中的贡献度，最后利用深度监督非对称聚合补充各提升块的梯度流，以避免模型在训练过程中产生梯度消失的问题。在DRIVE、STARE数据集上与近年来不同算法进行了对比，其中SP值分别为0.985 7、0.991 7，相较于第二名提升了0.2%、0.25%，均优于目前最新的算法结果。实验证明了提出的提升算法能较好地改善血管前期信息，一定程度上解决了信息稀释问题，提高了对视网膜血管的分割效果。

关键词: 视网膜血管, 提升算法, 概率分布注意力, 深监督非对称聚合

LYU Jia, MA Chao, CHENG Chao. Improved U-Net Network for Retinal Vascular Segmentation[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(3): 657-666.

吕佳, 马超, 程超. 改进的U-Net网络用于视网膜血管分割[J]. 计算机科学与探索, 2023, 17(3): 657-666.

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