Comparative study of the multi-atlas segmentation algorithm based on ANTs registration

Yan JIANG; Yu MA; Yue LU; Yuan WANG; Yuan-zhe LIANG; Xia LI

doi:10.37188/CJLCD.2020-0251

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Comparative study of the multi-atlas segmentation algorithm based on ANTs registration

Image Processing | 更新时间：2021-04-28

- Comparative study of the multi-atlas segmentation algorithm based on ANTs registration
- Chinese Journal of Liquid Crystals and Displays Vol. 36, Issue 5, Pages: 723-732(2021)
- 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：
- 基金信息：
  
  Supported by Natural Science Foundation of Ningxia Hui Autonomous Region(NZ1609)
- DOI：10.37188/CJLCD.2020-0251
  CLC： TP391.4
- Received：25 September 2020，
  
  Revised：13 November 2020，
  
  Published：2021-05
- 稿件说明：
移动端阅览
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：

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

Y CHEN , L Y SHI , Q J FENG , 等 . Artifact suppressed dictionary learning for low-dose CT image processing . IEEE Transactions on Medical Imaging , 2014 . 33 ( 12 ): 2271 - 2292 . DOI: 10.1109/TMI.2014.2336860 http://doi.org/10.1109/TMI.2014.2336860 .

X R YIN , Q L ZHAO , J LIU , 等 . Domain progressive 3D residual convolution network to improve low-dose CT imaging . IEEE Transactions on Medical Imaging , 2019 . 38 ( 12 ): 2903 - 2913 . DOI: 10.1109/TMI.2019.2917258 http://doi.org/10.1109/TMI.2019.2917258 .

吴磊 , 吕国强 , 赵晨 , 等 . 基于多尺度残差网络的CT图像超分辨率重建 . 液晶与显示 , 2019 . 34 ( 10 ): 1006 - 1012 . http://cjlcd.lightpublishing.cn/thesisDetails#10.3788/YJYXS20193410.1006 http://cjlcd.lightpublishing.cn/thesisDetails#10.3788/YJYXS20193410.1006 .

L WU , G Q LV , C ZHAO , 等 . CT image super-resolution reconstruction based on multi-scale residual network . Chinese Journal of Liquid Crystals and Displays , 2019 . 34 ( 10 ): 1006 - 1012 . http://cjlcd.lightpublishing.cn/thesisDetails#10.3788/YJYXS20193410.1006 http://cjlcd.lightpublishing.cn/thesisDetails#10.3788/YJYXS20193410.1006 .

陈雯艳. 基于ROI多图谱配准的海马磁共振图像分割[D]. 长沙: 湖南大学, 2012: 5-15.

CHEN W Y. Segmentation of hippocampus based on ROI atlas registration[D]. Changsha: Hunan University, 2012: 5-15. (in Chinese)

Y CHEN , J H MA , Q J FENG , 等 . Nonlocal prior Bayesian tomographic reconstruction . Journal of Mathematical Imaging and Vision , 2008 . 30 ( 2 ): 133 - 146 . DOI: 10.1007/s10851-007-0042-5 http://doi.org/10.1007/s10851-007-0042-5 .

S JOSHI , B DAVIS , M JOMIER , 等 . Unbiased diffeomorphic atlas construction for computational anatomy . Neuroimage , 2004 . 23 Suppl 1 S151 - S160 . http://new.med.wanfangdata.com.cn/Paper/Detail?id=PeriodicalPaper_JJ027007886 http://new.med.wanfangdata.com.cn/Paper/Detail?id=PeriodicalPaper_JJ027007886 .

D W SHATTUCK , R M LEAHY . BrainSuite: an automated cortical surface identification tool . Medical Image Analysis , 2002 . 6 ( 2 ): 129 - 142 . DOI: 10.1016/S1361-8415(02)00054-3 http://doi.org/10.1016/S1361-8415(02)00054-3 .

P ALJABAR , R A HECKEMANN , A HAMMERS , 等 . Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy . NeuroImage , 2009 . 46 ( 3 ): 726 - 738 . DOI: 10.1016/j.neuroimage.2009.02.018 http://doi.org/10.1016/j.neuroimage.2009.02.018 .

AWATE S P, ZHU P H, WHITAKER R T. How many templates does it take for a good segmentation?: Error analysis in multiatlas segmentation as a function of database size[C]// Second International Workshop on Multimodal Brain Image Analysis . Nice, France: Springer, 2012: 103-114.

W B TAO . Iterative narrowband-based graph cuts optimization for geodesic active contours with region forces (GACWRF) . IEEE Transactions on Image Processing , 2012 . 21 ( 1 ): 284 - 296 . DOI: 10.1109/TIP.2011.2160955 http://doi.org/10.1109/TIP.2011.2160955 .

芦玥 , 马瑜 , 王慧 , 等 . 基于U-Net的多图谱标签融合算法 . 液晶与显示 , 2019 . 34 ( 11 ): 1091 - 1103 . http://cjlcd.lightpublishing.cn/thesisDetails#10.3788/YJYXS20193411.1091 http://cjlcd.lightpublishing.cn/thesisDetails#10.3788/YJYXS20193411.1091 .

Y LU , Y MA , H WANG , 等 . Multi-atlaslabel fusion based on U-Net . Chinese Journal of Liquid Crystals and Displays , 2019 . 34 ( 11 ): 1091 - 1103 . http://cjlcd.lightpublishing.cn/thesisDetails#10.3788/YJYXS20193411.1091 http://cjlcd.lightpublishing.cn/thesisDetails#10.3788/YJYXS20193411.1091 .

夏瑞 , 马瑜 , 王文娜 , 等 . 基于重采样改进的多图谱分割算法 . 计算机工程与设计 , 2018 . 39 ( 8 ): 2587 - 2592, 2659 . https://www.cnki.com.cn/Article/CJFDTOTAL-SJSJ201808033.htm https://www.cnki.com.cn/Article/CJFDTOTAL-SJSJ201808033.htm .

R XIA , Y MA , W N WANG , 等 . Multi-atlas segmentation algorithm based on resampling . Computer Engineering and Design , 2018 . 39 ( 8 ): 2587 - 2592, 2659 . https://www.cnki.com.cn/Article/CJFDTOTAL-SJSJ201808033.htm https://www.cnki.com.cn/Article/CJFDTOTAL-SJSJ201808033.htm .

T VERCAUTEREN , X PENNEC , A PERCHANT , 等 . Diffeomorphic demons: efficient non-parametric image registration . NeuroImage , 2009 . 45 ( 1 Suppl 1 ): S61 - S72 . http://europepmc.org/abstract/med/19041946 http://europepmc.org/abstract/med/19041946 .

B B AVANTS , N J TUSTISON , G SONG , 等 . A reproducible evaluation of ANTs similarity metric performance in brain image registration . NeuroImage , 2011 . 54 ( 3 ): 2033 - 2044 . DOI: 10.1016/j.neuroimage.2010.09.025 http://doi.org/10.1016/j.neuroimage.2010.09.025 .

H Z WANG , S R DAS , J W SUH , 等 . A learning-based wrapper method to correct systematic errors in automatic image segmentation: consistently improved performance in hippocampus, cortex and brain segmentation . NeuroImage , 2011 . 55 ( 3 ): 968 - 985 . DOI: 10.1016/j.neuroimage.2011.01.006 http://doi.org/10.1016/j.neuroimage.2011.01.006 .

Q ZHENG , Y FAN . Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation . Proceedings. IEEE International Symposium on Biomedical Imaging , 2018 . 2018 154 - 157 . http://www.ncbi.nlm.nih.gov/pubmed/30079126 http://www.ncbi.nlm.nih.gov/pubmed/30079126 .

Alzheimer's disease neuroimaging initiative[EB/OL]. [2018-11-20] . http://adni.loni.usc.edu http://adni.loni.usc.edu .

Brain-Development. Brain atlases[EB/OL]. [2018-11-20] . http://biomedic.doc.ic.ac.uk/brain-development/ http://biomedic.doc.ic.ac.uk/brain-development/ .

JOHNSON H J, MCCORMICK M M, IBANEZ L. The ITK Software Guide Book 1: Introduction and Development Guidelines-Volume 1[M]. Clifton Park: Kitware, Inc., 2015.

H LIU , M YAN , E M SONG , 等 . Label fusion method based on sparse patch representation for the brain MRI image segmentation . IET Image Processing , 2017 . 11 ( 7 ): 502 - 511 . DOI: 10.1049/iet-ipr.2016.0988 http://doi.org/10.1049/iet-ipr.2016.0988 .

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Related Author

Yan JIANG

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Related Institution

School of Physics and Electronic-Electrical Engineering， Ningxia University

School of Information Science and Engineering， Wuhan University of Science and Technology

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