摘要
目的超声检查是诊断甲状腺疾病的主要影像学方法之一,但由于超声图像中斑点强度具有随机性、组织器官复杂等问题,导致甲状腺在不同数据源间的形态、大小和纹理差异性较大,容易导致观察者视觉疲劳。针对甲状腺超声成像存在斑点强度随机性以及周边组织复杂性的问题,为了更准确地描述出器官与病理性病变的解剖边界,提出一种基于频域增强和局部注意力机制的甲状腺超声分割网络。方法针对原始数据采用高低通滤波器获取高低频段的图像信息,整合高频段细节特征与低频段边缘特征,增强图像前背景的对比度,降低图像间的差异性。根据卷积网络中网络深度所提取特征信息量的不同,采用局部注意力机制对高低维特征信息进行自适应激活,增强低维特征的细节信息,弱化对非目标区域的关注,增强高维特征的全局信息,弱化冗余信息对网络的干扰,增强前背景分类以及对非显著性目标检测的能力。采用金字塔级联空洞卷积获取不同感受野的特征信息,解决数据源间图像差异较大的问题。结果实验结果表明,本文方法在11~16 MHz时采集的16个手绘甲状腺超声公开数据集中,通过10折交叉验证显示准确率为0.989,召回率为0.849,精准率为0.940,Dice系数为0.812,效果优于当前其他医学图像分割网络。通过消融实验,证明本文的几个模块对超声图像分割确实具有一定的提升效果。结论本文所提分割网络,结合深度学习模型及传统图像处理模型的优点,能较好地处理超声图像随机斑点并且提升非显著性组织分割效果。
Objective Ultrasound is a main imaging method used for the diagnosis of thyroid diseases.It is convenient for the diagnosis of medical results through the real-time study of its internal anatomical structure.In computer vision,the segmentation of image tissue and organ is the pre background classification of the pixels in the image.The final segmentation image boundary is the combination of the target pixels.The research on medical image segmentation has received much attention,which is mainly divided into two ideas,where the first idea is to obtain the target area by analyzing the pixel value of a given image through computer vision technology.However,the generalization ability of the given image analysis is poor,and the segmentation effect is unremarkable because of the interference of random noise in the ultrasonic image.The second idea is to use deep learning for obtaining the target area through the background information before deep convolution classification.However,the target area may be insignificant using the depth learning model because of the complexity of tissue and organs,the evident surrounding tissues,and the lack of background information before the image,making the abstract features obtained by the depth network mostly the surrounding non target area and causing the segmentation effect of the original target unideal.A thyroid image is different in shape,size,and texture among different data sources.To solve the two problems,a thyroid ultrasound segmentation network based on frequency domain enhancement and local attention mechanism is proposed to solve the problem of random noise interference and insignificant target.Method First,high and low pass filters are used to obtain the image information of high-and low-frequency bands,and the detail features of high frequency band and the edge feature of low frequency band are integrated to enhance the contrast of background and reduce the difference between images.Second,a local attention mechanism is used to adaptively activate the high-and low-dimens
作者
胡屹杉
秦品乐
曾建潮
柴锐
王丽芳
Hu Yishan;Qin Pinle;Zeng Jianchao;Chai Rui;Wang Lifang(Shanxi Medical Imaging and Data Analysis Engineering Research Center,North University of China,Taiyuan 030051,China;College of Big Data,North University of China,Taiyuan 030051,China;Shanxi Medical Imaging Artificial Intelligence Engineering Technology Research Center,North University of China,Taiyuan 030051,China)
出处
《中国图象图形学报》
CSCD
北大核心
2020年第10期2195-2205,共11页
Journal of Image and Graphics
基金
山西省工程技术研究中心建设项目(201805D121008)
山西省研究生创新项目(2020SY381)
中北大学研究生科技立项项目(20191634)
关键词
图像分割
频域分析
注意力机制
空洞卷积
超声影像
image segmentation
frequency domain analysis
attention mechanism
dilate convolution
ultrasound image
