基于注意力机制的眼底血管图像分割算法  

Fundus Vascular Image Segmentation Algorithm Based on Attention Mechanism

作　　者：王利彬 王树梅[1] WANG Libin;WANG Shumei(College of Computer Science and Technology,Jiangsu Normal University,Xuzhou,Jiangsu 221116,China)

机构地区：[1]江苏师范大学计算机科学与技术学院,江苏徐州221116

出　　处：《计算机科学》2024年第S02期349-354,共6页Computer Science

摘　　要：为了缩小编码器-解码器结构存在的语义差距,提出了一种基于注意力机制的医学图像分割算法。首先,使用CBAM注意力模块,通过注意力机制模块增强模型进行医学图像的特征提取;其次,将CBAM模块输出的特征图作为文中所提出的特征细化模块的输入,用于恢复由于下采样所丢失的血管细节信息;最后,使用一种尺度注意力模块,将不同尺度的特征图所具有的特征结合起来形成最终的预测。通过与当下流行的眼底血管分割算法进行对比,所提算法在DRIVE数据集上的mIoU最高提升了2~3个百分点,最接近的也提升了0.4个百分点,证明了所提模型能够有效提升分割精度,对于恢复细微血管像素有着较好的效果。In order to narrow the semantic gap between the encoder-decoder structure,a medical image segmentation algorithm based on attention mechanism is proposed.Firstly,the CBAM is used to enhance the model for feature extraction of medical images through the attention mechanism module.Secondly,Using the feature map output by the CBAM module as the input of the feature refinement module proposed in this paper,it is used to restore the vascular detail information lost due to downsampling,so as to narrow the semantic gap.Finally,a scale attention module is used to combine the features of feature maps at different scales to form the final prediction.By comparing with the cunrrently popular retinal vessel segmentation algorithm,the proposed algorithm can improve the mIoU by up to 2.3%on the DRIVE dataset,with the closest approach also improving by 0.4%.This de-monstrates that the proposed model can effectively enhance segmentation accuracy and achieve good results in restoring subtle vascular pixels.

关 键 词：医学图像 图像分割 U-Net 注意力机制 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]