摘要
目的高效的肝肿瘤计算机断层扫描(computed tomography,CT)图像自动分割方法是临床实践的迫切需求,但由于肝肿瘤边界不清晰、体积相对较小且位置无规律,要求分割模型能够细致准确地发掘类间差异。对此,本文提出一种基于特征选择与残差融合的2D肝肿瘤分割模型,提高了2D模型在肝肿瘤分割任务中的表现。方法该模型通过注意力机制对U-Net瓶颈特征及跳跃链接进行优化,为符合肝肿瘤分割任务特点优化传统注意力模块进,提出以全局特征压缩操作(global feature squeeze,GFS)为基础的瓶颈特征选择模块,即全局特征选择模块(feature selection module,FS)和邻近特征选择模块(neighbor feature selection module,NFS)。跳跃链接先通过空间注意力模块(spatial attention module,SAM)进行特征重标定,再通过空间特征残差融合(spatial feature residual fusion module,SFRF)模块解决前后空间特征的语义不匹配问题,在保持低复杂度的同时使特征高效表达。结果在LiTS(liver tumor segmentation)公开数据集上进行组件消融测试并与当前方法进行对比测试,在肝脏及肝肿瘤分割任务中的平均Dice得分分别为96.2%和68.4%,与部分2.5D和3D模型的效果相当,比当前最佳的2D肝肿瘤分割模型平均Dice得分高0.8%。结论提出的FSF-U-Net(feature selection and residual fusion U-Net)模型通过改进的注意力机制与优化U-Net模型结构的方法,使2D肝肿瘤分割的结果更加准确。
Objective Liver cancer is currently one of the most common cancers with the highest mortality rate in the world.Computed tomography(CT)is a commonly used clinical tumor diagnosis method.It can aid to designate targeted treatment plans based on the shape and location of the tumor measurement.Manual segmentation of CT images has challenged issues,such as low efficiency and the influence of doctors’experience.Hence,an efficient automatic segmentation method is focused on in clinical practice.Liver treatment can benefit from accurate and fast automatic segmentation methods.Due to the low contrast of soft tissue in CT images,the shape and position of liver tumors are highly variable,and the boundaries of liver tumor regions are difficult to identify,most of the tumors area are relatively small,so automatic liver tumor segmentation is a challenging task in practice.The segmentation model is capable to discover the differences between each class accurately.Deep-learning-based models can be divided into three categories:2 D,2.5 D and 3 D,respectively.The traditional channel attention module uses the global average pooling(GAP)to squeeze feature map.This operation calculates the average value of the feature map straightforward,resulting in the loss of spatial information on the feature map.The model can focus on the correlation amongst channels and ignore the spatial features of each channel,but segmentation task is related to the spatial information.This research illustrated a liver tumor 2 D segmentation model with feature selection and residual fusion to improve the performance of low-complexity models.Method The attention-mechanism-based model optimizes U-Net bottleneck features and redesigned skip connections.In order to meet the characteristics of liver tumor segmentation tasks,we optimized the traditional attention module.Our demonstration facilates the global feature squeeze(GFS)substitute of the global average pooling(GAP)in the traditional attention module.A designed bottleneck feature selection module is base
作者
乔伟晨
黄冕
刘利军
黄青松
Qiao Weichen;Huang Mian;Liu Lijun;Huang Qingsong(Faculty of Information Engineering and Automation,Kunming University of Science and Technology,Kunming 650500,China;Yunnan Land and Resources Vocational College Information Center,Kunming 652501,China;School of Information,Yunnan University,Kunming 650091,China;Computer Technology Application Key Laboratory of Yunnan Province,Kunming 650500,China)
出处
《中国图象图形学报》
CSCD
北大核心
2022年第3期838-849,共12页
Journal of Image and Graphics
基金
国家自然科学基金项目(81860318,81560296)
云南省计算机技术应用重点实验室开放基金项目(2020106)。
