摘要
为提高海马体多图谱分割的精度和时间效率,提出基于高等标准化工具(Advanced Normalization Tools,ANTs)配准的多图谱分割算法。为降低数据规模,在预处理阶段,提取以海马体为中心的立方体box。在配准阶段,提出用ANTs代替重采样环节,再利用微分同胚Demons算法的平滑性、拓扑保持性以及连续性进行精配准。在标签融合阶段,采用加权平均(Majority Voting,MV)算法、基于生成模型约束的GraphCut标签融合(Generative Model,GM)算法、度量学习(Metric Learning,ML)算法以及半监督标签传播随机森林(Integrating Semi-Supervised Label Propagation and Random Forests,RF-SSLP)算法做对比。实验结果表明,用ANTs代替重采样后,可分别提高MV、GM、ML以及RF-SSLP四种融合算法的精度,同时,通过以上4种融合算法的对比发现,基于ANTs配准的半监督标签传播随机森林算法分割精度最高,相较于MV、GM和ML三种融合算法精度提高了3%~5%。
In order to improve the accuracy and time efficiency of hippocampus multi-atlas segmentation,an algorithm based on Advanced Normalization Tools(ANTs)registration was proposed.In order to reduce the data size,a box with hippocampus as the center was extracted in the preprocessing stage.In the registration stage,ANTs were used to replace the resampling link,and the smoothness,topological retention and continuity of the differential Diffeomorphic Demons algorithm were used to perform accurate registration.In the tag fusion stage,four fusion algorithms including weighted average(Majority Voting,MV)algorithm,GraphCut tag fusion(Generative Model,GM)algorithm based on generated model constraints,metric learning(Metric Learning,ML)algorithm and semi-supervised tag propagation random forest(Integrating Semi-Supervised Label Propagation and Random Forests,RF-SSLP)algorithm were compared.The experimental results show that after replacing resampling with ANTs,the accuracy of four fusion algorithms including MV,GM,ML and RF-SSLP can be improved,respectively.Meanwhile,through the comparison of the above four fusion algorithms,it is found that the semi-supervised random forest algorithm based on ANTs registration has the highest segmentation accuracy,which is improved by 3%~5%compared with MV,GM and ML fusion algorithms.
作者
江妍
马瑜
芦玥
王原
梁远哲
李霞
JIANG Yan;MA Yu;LU Yue;WANG Yuan;LIANG Yuan-zhe;LI Xia(School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, China)
出处
《液晶与显示》
CAS
CSCD
北大核心
2021年第5期723-732,共10页
Chinese Journal of Liquid Crystals and Displays
基金
宁夏自然科学基金(No.NZ1609)
宁夏高等学校科学研究项目(No.NGY2016015)
2018年宁夏研究生教育教学改革研究与实践项目(No.YJG201811)
宁夏大学研究生创新研究项目(No.GIP2019060)。
关键词
海马体
多图谱
高等标准化工具
融合算法
hippocampus
multi-atlas
advanced normalization tools
fusion algorithm
