摘要
针对甲状腺结节尺寸多变、超声图像结节边缘不清晰等特征导致人工诊断筛查过程中出现的误诊和漏诊的现象,提出了一种基于多尺度注意力的MSA-UNet甲状腺结节超声分割方法(mutil-scale attention UNet,MSA-UNet).该算法首先使用不同空洞率的空洞卷积提取甲状腺病灶特征信息,之后将不同尺度的特征信息进行特征融合,解决不同甲状腺结节大小对超声图像分割结果的影响。考虑到位置关系信息学习和深层次语义特征筛选后的特征对分割模型的影响,通过使用通道注意力机制使网络模型更加专注于更有用的特征信息,从而提高甲状腺结点的分割精度,实现病灶区域的精细分割。实验结果表明,该方法在甲状腺超声图像数据集上召回率达87%,分割精度为86.1%,Dice值为84.6%,较现有的深度学习方法有较高的提升,可为甲状腺结节的检测诊疗提供新的研究思路。
To tackle the misdiagnosis and missed diagnosis problem in the process of manual diagnosis and screening due to the variable size of thyroid nodule and fuzzy edge of nodule in ultrasonic image, in this paper, a mutil-scale attention UNet(MSA-UNet) method for ultrasonic segmentation of thyroid nodule was proposed. The algorithm first extracts the characteristic information of thyroid lesions by using cavity convolution with different void rates, and then fuses the characteristic information of different scales to solve the influence of different thyroid nodule sizes on ultrasonic image segmentation. Considering the influence of location relation information learning and deep semantic feature screening on the segmentation model, channel attention mechanism is used to make the network model more focused on more useful feature information, to improve the segmentation accuracy of thyroid nodes and achieve fine segmentation of lesion region. Experimental results show that the recall rate in thyroid ultrasound image data set is 87%, the segmentation accuracy is 86.1%, and the Dice value is 84.6%, higher than those of existing deep learning method. This work can provide a new research idea for the detection and diagnosis of thyroid nodules.
作者
周晓松
赵涓涓
ZHOU Xiaosong;ZHAO Juanjuan(College of Information and Computer,Taiyuan University of Technology,Jinzhong 030600,China)
出处
《太原理工大学学报》
CAS
北大核心
2022年第6期1134-1142,共9页
Journal of Taiyuan University of Technology
基金
国家自然科学基金资助项目(61972274)。
关键词
图像分割
深度学习
注意力机制
神经网络
U型网络
image segmentation
deep learning
attention mechanism
neural network
U network
