摘要
肝脏疾病是医学上最常见的疾病之一,对其进行精确的分割是辅助肝脏疾病诊断及手术规划的必要步骤。然而,由于肝脏CT图像的复杂性,肝脏分割仍然是一个极具挑战性的问题。以往的研究大多简单地使用拼接或求和操作来融合不同语义,导致无法充分利用其互补性。针对这一问题,提出了一种基于多尺度特征融合和双重注意力机制的网络模型MD-AUNet。首先利用分层多尺度注意力下采样模块中分层级的双重注意力机制有效地融合不同尺度特征信息,提取富含空间信息的特征表示。然后通过全局注意力上采样模块获取高层特征的全局上下文用于对低层特征信息加权,从而选择更为精确的空间信息。同时在网络训练时采用深层监督策略,以学习不同解码层的层次表示。最后提出了一种简洁有效的后处理方法,用于进一步细化MD-AUNet粗分割结果。在医院采集的肝脏数据集(经专家手动标注)上的实验结果表明,所提算法在主观视觉感受和客观评价指标方面均优于其他现有肝脏分割算法,其平均像素精度、平均交并比和Dice相似系数分别为97.6%,95.4%和95.5%。
Liver disease is one of the most common diseases in medicine,and accurate segmentation of liver disease is a necessary step to assist liver disease diagnosis and surgical planning.However,liver segmentation is still a challenging task due to the complexity of liver CT images.With the deepening of research,people begin to consider combining high-level semantics with low-level semantics to further enhance the segmentation effect.However,most of previous studies simply use splicing or summation operation to fuse different semantics,resulting in failure to make full use of its complementarity.To solve the above problems,a network(MD-AUNet)based on multi-scale feature fusion and dual attention mechanism is proposed in this paper.Firstly,the hierarchical dual attention mechanism in the hierarchical multi-scale attention down-sampling module(HAM)is used to effectively fuse feature information of different scales and extract feature representations rich in spatial information.Then,the global context of high-level features is obtained through the global attention up-sampling module(GAM)for weighting the low-level feature information,so as to select more accurate spatial information.At the same time,deep supervision strategy is used in network training to learn the hierarchical representation of different decoding layers.Moreover,a concise and effective post-processing method is proposed to refine the coarse segmentation result of MD-AUNet.Experimental results on the liver datasets collected by the hospital(manually annotated by experts)demonstrate that the proposed algorithm is superior to other existing liver segmentation algorithms in subjective visual perception and objective evaluation indicators,and its mean pixel accuracy,mean IoU and Dice are97.6%,95.4%,and 95.5%respectively.
作者
黄扬林
胡凯
郭建强
彭诚
HUANG Yang-lin;HU Kai;GUO Jian-qiang;Peng Cheng(Key Laboratory of Intelligent Computing&Information Processing of Ministry of Education,Xiangtan University,Xiangtan,Hunan 411105,China;School of Computer Science&School of Cyberspace Science,Xiangtan University,Xiangtan,Hunan 411105,China)
出处
《计算机科学》
CSCD
北大核心
2022年第S02期549-557,共9页
Computer Science
基金
国家自然科学基金(62272404)
湖南省大学生创新创业训练计划项目(S202010530031)
