基于语义与形态特征融合的语义分割网络

Semantic Segmentation Network Based on Semantic and Morphological Feature Fusion

视网膜血管检测有助于医生诊断视网膜疾病,而以往基于特征融合的算法难以解决视网膜血管检测中出现的漏分割问题,且分割准确率较低.本文对特征融合方式做出进一步探索,并提出一种基于语义与形态特征融合的算法,通过挖掘输入特征中蕴含的语义与形态信息,建模特征间的相关关系.随后,使用特征融合模块实现多模态特征自适应地融合.在公开数据集DRIVE以及STARE上的实验结果表明,文章算法优于现有的语义分割模型,尤其在敏感性上,比传统U-Net网络提升了8.20%.

Retinal blood vessel detection is helpful for doctors to diagnose retinal diseases,but the previous algorithm based on feature fusion is difficult to solve the problem of missed segmentation in retinal blood vessel detection,and the segmentation accuracy is low.This paper further explores the feature fusion method and proposes an algorithm based on the fusion of semantic and morphological features.It models the correlation between features by mining the semantic and morphological information contained in the input features.Then,the feature fusion module realizes the adaptive fusion of multi-modal features.The experimental results on the public datasets DRIVE and STARE show that,the article algorithm is better than the existing semantic segmentation model,especially in sensitivity,which is8.20%higher than the traditional UNet.