基于通道注意力机制和U-net的医学图像分割方法  

Medical image segmentation method based on channel attention mechanism and U-net

作　　者：王枫 吕泽均[1] Wang Feng;Lv Zejun(College of Computer Science(College of Software),Sichuan University,Chengdu,Sichuan 610000,China)

机构地区：[1]四川大学计算机学院,四川成都610000

出　　处：《计算机时代》2021年第5期64-67,72,共5页Computer Era

摘　　要：随着人工智能和医学大数据的发展,基于深度学习的医学图像分割技术因具有重要的应用价值和前景,已经成为目前的研究热点。为了增强特征图的语义信息,在U-net网络的基础上引入通道注意力机制,对U-net生成的特征逐通道进行压缩,将压缩后的特征逐通道计算权重,然后将该权重与原始特征相乘得出最终的特征。通过在两个不同器官的医学图像数据集上进行实验,Dice系数相较于原始U-net网络分别提高了2.7%和1.8%,验证了该方法的可行性和有效性。With the development of artificial intelligence and medical big data,medical image segmentation technology based on deep learning,for its important application value and prospects,has become a current research hotspot.In order to enhance the semantic information of the feature map,a channel attention mechanism is introduce for U-net network to compress the features generated by U-net channel by channel,calculate the weights of the compressed features channel by channel,and then get the final features by multiplying the weights by the original features.The experiment on medical image data set of two different organs show that the Dice coefficient is increased by 2.7%and 1.8%respectively compared with the original U-net network,which verifies the feasibility and effectiveness of the method.

关 键 词：深度学习 U-net网络 通道注意力机制 医学图像分割 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]