基于U-Net多尺度自校准注意力视网膜分割算法

Based on U-Net multi-scale self-calibrating attention retinal segmentation algorithm

针对视网膜细小血管分割精度低的问题,提出一种融合可伸缩级联模块、Transformer和自校准注意力的改进U-Net算法以提高细小血管分割精度。首先在编码阶段利用可伸缩级联模块,先行学习复杂多变的视网膜血管拓扑结构。然后在解码阶段提出一种自校准注意力机制,利用多尺度挤压激励模块,自适应对特征图通道和空间之间特征重要性进行校准,增强目标区域特征响应,抑制背景噪声。最后使用Transformer特征提取块,提高特征空间映射能力。基于DRIVE和CHASEDB1数据集的实验结果表明,所提算法准确率分别为96.49%和96.67%,灵敏度分别为83.75%和83.30%,特异性分别为98.28%和98.01%,AUC分别为0.987 1和0.987 2,所提算法的整体性能优于现有算法,各模块能够有效提高细小血管分割能力。

Aiming at the problem of the low accuracy of fine retinal vessel segmentation, this paper proposed improved U-Net methods combining scalable cascade modules, Transformer and self-calibrated attention modules to improve the accuracy of fine retinal vessel segmentation.Firstly, this paper used scalable cascaded modules in the encoding stage to enable the learning of complex and variable retinal vessel structures.Secondly, in the decoding stage, this paper proposed a self-calibration attention mechanism, which used the multi-scale compression excitation module to adaptively recalibrate the blood vessel from channel and spatial of features, it could enhance the feature response of the target area, and suppressed the background noise.Finally, the Transformer feature extraction block improved the capability of feature space mapping.This proposed method tested on public datasets, i.e.the DRIVE and CHASEDB1.The experimental results of proposed method show that the accuracy of the retinal vessel segmentation from the two datasets reach 96.49%/96.67%,the sensitivity reach 83.75%/83.30%,the specificity reach 98.28%/98.01% and the AUC reach 0.987 1/0.987 2,respectively.The performance of proposed method is better than most of existing methods, and each modules can improve the ability of fine retinal vessel segmentation.