基于ANTs配准的多图谱分割算法比较研究

江妍; 马瑜; 芦玥; 王原; 梁远哲; 李霞

doi:10.37188/CJLCD.2020-0251

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基于ANTs配准的多图谱分割算法比较研究

图像处理 | 更新时间：2021-04-28

- 基于ANTs配准的多图谱分割算法比较研究
- Comparative study of the multi-atlas segmentation algorithm based on ANTs registration
- 液晶与显示 2021年36卷第5期页码：723-732
- 作者机构：
  
  宁夏大学物理与电子电气工程学院, 宁夏银川 750021
- 作者简介：
  
  [ "江妍(1994-), 女, 河北石家庄人, 硕士研究生, 2017年于河北工业大学获得学士学位, 主要从事模式识别与图像处理方向的研究。E-mail:1322415165@qq.com" ]
  [ "马瑜(1974-), 男, 宁夏西吉人, 博士, 教授, 2013于西北工业大学获得博士学位, 主要从事计算机视觉与模式识别方向的研究。E-mail: mayu95@163.com" ]
- 基金信息：
  
  宁夏自然科学基金(NZ1609);宁夏高等学校科学研究项目(NGY2016015);2018年宁夏研究生教育教学改革研究与实践项目(YJG201811);宁夏大学研究生创新研究项目(GIP2019060)
- DOI：10.37188/CJLCD.2020-0251
  中图分类号： TP391.4
- 收稿日期：2020-09-25，
  
  修回日期：2020-11-13，
  
  纸质出版日期：2021-05
- 稿件说明：
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江妍, 马瑜, 芦玥, 等. 基于ANTs配准的多图谱分割算法比较研究[J]. 液晶与显示, 2021,36(5):723-732.

Yan JIANG, Yu MA, Yue LU, et al. Comparative study of the multi-atlas segmentation algorithm based on ANTs registration[J]. Chinese journal of liquid crystals and displays, 2021, 36(5): 723-732.
江妍, 马瑜, 芦玥, 等. 基于ANTs配准的多图谱分割算法比较研究[J]. 液晶与显示, 2021,36(5):723-732. DOI： 10.37188/CJLCD.2020-0251.

Yan JIANG, Yu MA, Yue LU, et al. Comparative study of the multi-atlas segmentation algorithm based on ANTs registration[J]. Chinese journal of liquid crystals and displays, 2021, 36(5): 723-732. DOI： 10.37188/CJLCD.2020-0251.

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%。

Abstract

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

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.

关键词

Keywords

references

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