摘要
目的心脏磁共振成像(magnetic resonance imaging,MRI)的自动分割技术有利于在临床诊断中评估心脏的功能参数。然而由于心脏磁共振成像技术产生的图像边界不清晰、各向分辨率异性等特性,现有的大多数方法依旧存在类内不确定、类间不清晰问题。针对这一问题,提出了一种利用时间信息进行特征增强,并利用空间信息进行特征矫正的多输入、多分支和多任务的分割网络(spatio-temporal UNet,ST UNet)。方法为充分获取动态心脏MRI图像的时间信息,提出了全新的时间增强编码模块,将需要进行分割的目标帧和一段包含了目标帧的连续时间片段作为关键序列一同输入网络。关键序列用于获取更丰富的时间特征,目标帧提供更精准的边缘特征。为了聚集更多有益的特征,更好地融合时域特征和边缘特征,采用可变形全局连接代替传统的长连接,为网络的解码部分提供更广泛的多维特征信息。在训练过程中额外学习空间方向场特征,并使用该特征对原有的分割结果进行矫正。结果在ACDC(Automated Cardiac Diagnosis Challenge)心脏分割挑战中,以Dice系数和HD(Hausdorff distance)距离为评价指标,该方法在左心室、右心室和左心肌分割的平均Dice系数分别为95%、91.5%和91%,HD距离的平均值分别为6.77、11.39和8.54。结论实验表明,提出的新型网络能够充分地利用心脏MRI图像的时空信息,有效地提升目标器官的分割效果,更有助于医生对于心脏诊断。
Objective The segmentation of cardiac dynamic magnetic resonance imaging(MRI)is essential to evaluate cardiac functional parameters in clinical diagnosis.Based on the segmentation results,the qualified analysis can be obtained the myocardial mass and thickness,ejection fraction,ventricular volume and other diagnostic indicators effectively.Currently,the method of heart segmentation is still limited to manual segmentation.This method is time-consuming and human behavior-oriented.Therefore,the issue of automatic and accurate cardiac-MRI(CMRI)segmentation has been focused on.However,due to the non-uniform magnetic field intensity,artifacts resulted blurred boundaries in the process of imaging.Organs of different subjects vary greatly,especially the variable shape and size of the right ventricle,which tends to produce volume effect.In addition,dynamic magnetic resonance imaging is featured based on a small scale of short axis sections with thick slice,which results in low short axis resolution and sparse information of the image.As the existing data sets only including ground truth of dynamic MRI images at two sites in the end of systole and end of diastole,the existing networks usually only take the images at these two sites as segmentation objects,which ignores the information of dynamic MRI images timescale.Hence,automatic segmentation of dynamic cardiac MRI images is challenged of the issues of intra-class uncertainty and inter-class imbalance.This illustration demonstrates a spatio-temporal multi-scale UNet that makes use of time information to conduct feature enhancement and spatial information to get feature correction.Method First,a new time-enhanced coding path is developed in order to fully obtain the time information of dynamic CMRI images,which consists of two branches are those of the target frame branch and the key sequence branch.The target frame is the image to be segmented,and the key sequence is the consistent time series containing the target frame.The key sequence is used to obtain richer time fea
作者
徐佳陈
肖志勇
Xu Jiachen;Xiao Zhiyong(School of Artificial Intelligence and Computer Science,Jiangnan University,Wuxi 214122,China)
出处
《中国图象图形学报》
CSCD
北大核心
2022年第3期862-872,共11页
Journal of Image and Graphics
基金
江苏省自然科学基金优秀青年项目(BK20190079)。
