摘要
Objective For computer-aided Chinese medical diagnosis and aiming at the problem of insufficient segmentation,a novel multi-level method based on the multi-scale fusion residual neural network(MF2ResU-Net)model is proposed.Methods To obtain refined features of retinal blood vessels,three cascade connected UNet networks are employed.To deal with the problem of difference between the parts of encoder and decoder,in MF2ResU-Net,shortcut connections are used to combine the encoder and decoder layers in the blocks.To refine the feature of segmentation,atrous spatial pyramid pooling(ASPP)is embedded to achieve multi-scale features for the final segmentation networks.Results The MF2ResU-Net was superior to the existing methods on the criteria of sensitivity(Sen),specificity(Spe),accuracy(ACC),and area under curve(AUC),the values of which are 0.8013 and 0.8102,0.9842 and 0.9809,0.9700 and 0.9776,and 0.9797 and 0.9837,respectively for DRIVE and CHASE DB1.The results of experiments demonstrated the effectiveness and robustness of the model in the segmentation of complex curvature and small blood vessels.Conclusion Based on residual connections and multi-feature fusion,the proposed method can obtain accurate segmentation of retinal blood vessels by refining the segmentation features,which can provide another diagnosis method for computer-aided Chinese medical diagnosis.
目的为解决计算机辅助中医诊断中图像分割精确问题,提出一种基于多尺度的融合冗余连接的多层结构模型的新型神经网络模型(MF2ResU-Net)。方法为获得精细化的血管特征,提出将三个U-Net模型进行级联。为解决U-Net模型中编解码部分语义连接差异性问题,MF2ResU-Net提出利用shortcut连接编解码部分。为了更加精细化分割特征,算法将空洞空间金字塔池化模型嵌入编解码结构中,最终形成所提网络。结果MF2ResU-Net算法在视网膜血管数据集CHASE DB1和DRIVE中,利用指标敏感度(Sen)、特异性(Spe)、精确性(ACC)和线下面积(AUC)分别取得了0.8013和0.8102、0.9842和0.9809、0.9700和0.9776,以及0.9797和0.9837。这些结果表明本方法优于对比方法,并且对于视网膜血管分割,本方法具有较高的有效性及鲁棒性。结论本文所提的基于冗余连接的多特征融合深度学习网络模型可通过获得更加精细化特征获取的方式获得更加准确的视网膜血管分割结果,可为计算机辅助中医诊断提供一种诊断方法提取途径。
基金
Key R&D Projects in Hebei Province(22370301D)
Scientific Research Foundation of Hebei University for Distinguished Young Scholars(521100221081)
Scientific Research Foundation of Colleges and Universities in Hebei Province(QN2022107)。
