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基于无监督学习的多模态可变形配准

马腾宇 李孜 刘日升 樊鑫 罗钟铉

马腾宇, 李孜, 刘日升, 等 . 基于无监督学习的多模态可变形配准[J]. 北京航空航天大学学报, 2021, 47(3): 658-664. doi: 10.13700/j.bh.1001-5965.2020.0449
引用本文: 马腾宇, 李孜, 刘日升, 等 . 基于无监督学习的多模态可变形配准[J]. 北京航空航天大学学报, 2021, 47(3): 658-664. doi: 10.13700/j.bh.1001-5965.2020.0449
MA Tengyu, LI Zi, LIU Risheng, et al. Multimodal deformable registration based on unsupervised learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 658-664. doi: 10.13700/j.bh.1001-5965.2020.0449(in Chinese)
Citation: MA Tengyu, LI Zi, LIU Risheng, et al. Multimodal deformable registration based on unsupervised learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47(3): 658-664. doi: 10.13700/j.bh.1001-5965.2020.0449(in Chinese)

基于无监督学习的多模态可变形配准

doi: 10.13700/j.bh.1001-5965.2020.0449
基金项目: 

国家自然科学基金 61922019

国家自然科学基金 61672125

国家自然科学基金 61733002

国家自然科学基金 61772105

兴辽英才计划 XLYC1807088

详细信息
    作者简介:

    马腾宇   男,硕士研究生。主要研究方向:基于深度学习的多模态医学图像配准

    刘日升   男,博士,教授,博士生导师。主要研究方向:深度学习、计算机视觉、最优化方法

    通讯作者:

    刘日升, E-mail: rsliu@dlut.edu.cn

  • 中图分类号: TP183

Multimodal deformable registration based on unsupervised learning

Funds: 

National Natural Science Foundation of China 61922019

National Natural Science Foundation of China 61672125

National Natural Science Foundation of China 61733002

National Natural Science Foundation of China 61772105

Liaoning Revitalization Talents Program XLYC1807088

More Information
  • 摘要:

    针对医学图像配准问题,传统方法提出通过解决优化问题进行配准,但计算成本高、运行时间长。深度学习方法提出使用网络学习配准参数,从而进行配准并在单模态图像上取得高效性能。但在多模态图像配准时,不同模态图像的强度分布未知且复杂,大多已有方法严重依赖标签数据,现有方法不能完全解决此问题。提出一种基于无监督学习的深度多模态可变形图像配准框架。该框架由基于损失映射量的特征学习和基于最大后验概率的变形场学习组成,借助空间转换函数和可微分的互信息损失函数实现无监督训练。在MRI T1、MRI T2以及CT的3D多模态图像配准任务上,将所提方法与现有先进的多模态配准方法进行比较。此外,还在最新的COVID-19的CT数据上展示了所提方法的配准性能。大量结果表明:所提方法与其他方法相比,在配准精度上具有竞争优势,并且大大减少了计算时间。

     

  • 图 1  由基于匹配量的特征学习和基于最大后验概率的变形场学习组成的无监督多模态配准框架

    Figure 1.  Unsupervised multimodal registration framework composed of feature learning based on matching amount and deformation field learning based on maximum posterior probability

    图 2  MRI T2->MRI T1配准结果可视化

    Figure 2.  Visualization of MRI T2->MRI T1 registration results

    表  1  MRI T1-MRI T2 RMSE配准结果

    Table  1.   MRI T1-MRI T2 RMSE registration results

    方法 配准方向
    MRI T2->MRI T1 MRI T1->MRI T2
    affined 17.549(3.116) 17.549(3.116)
    VoxelMorph 16.844(3.276) 17.545(2.982)
    Elastix 17.731(3.140) 17.717(3.111)
    ANTs (SyN) 16.932(3.141) 17.206(3.078)
    本文 16.725(3.303) 16.915(3.223)
    下载: 导出CSV

    表  2  CT-MRI配准结果

    Table  2.   CT-MRI registration results

    方法 RMSE
    affined 29.932(0.819)
    VoxelMorph 28.196(4.374)
    Elastix 27.252(0.961)
    ANTs (SyN) 29.401(1.021)
    本文 25.886(0.374)
    下载: 导出CSV

    表  3  CT-CT配准结果

    Table  3.   CT-CT registration results

    方法 Dice RMSE NCC Grad Det-Jac/10-3
    affined 0.464(0.091) 32.714(1.694) 0.180(0.008)
    VoxelMorph 0.636(0.109) 21.086(2.313) 0.224(0.016) 4.497(1.058)
    Elastix 0.522(0.123) 33.874(2.191) 0.112(0.021)
    ANTs (SyN) 0.611(0.233) 31.382(4.169) 0.133(0.063)
    本文-L1_cost 0.654(0.077) 30.135(3.336) 0.285(0.014) 0.866(0.004)
    本文-Prob 0.678(0.094) 28.617(3.232) 0.283(0.012) 0.812(0.005)
    本文 0.708(0.086) 27.461(3.162) 0.287(0.014) 0.812(0.004)
    下载: 导出CSV

    表  4  MRI T2->MRI T1配准运行时间

    Table  4.   MRI T2->MRI T1 registration running time

    方法 时间/s
    VoxelMorph 0.406
    Elastix 6.595
    SyN 20.038
    本文 0.281
    下载: 导出CSV
  • [1] MAINTZ J B A, VIERGEVER M A. A survey of medical image registration[J]. Medical Image Analysis, 1998, 2(1): 1-36. doi: 10.1016/S1361-8415(98)80001-7
    [2] ASHBURNER J. A fast diffeomorphic image registration algorithm[J]. NeuroImage, 2007, 38(1): 95-113. doi: 10.1016/j.neuroimage.2007.07.007
    [3] GUIZAR-SICAIROS M, THURMAN S T, FIENUP J R. Efficient subpixel image registration algorithms[J]. Optics Letters, 2008, 33(2): 156-158. doi: 10.1364/OL.33.000156
    [4] MAES F, VANDERMEULEN D, SUETENS P. Medical image registration using mutual information[J]. Proceedings of the IEEE, 2003, 10(91): 1699-1722. http://doi.ieeecomputersociety.org/resolve?ref_id=doi:10.1109/JPROC.2003.817864&rfr_id=trans/tp/2009/03/ttp2009030475.htm
    [5] D'AGOSTINO E, MAES F, VANDERMEULEN D, et al. A viscous fluid model for multimodal non-rigid image registration using mutual information[J]. Medical Image Analysis, 2003, 7(4): 565-575. doi: 10.1016/S1361-8415(03)00039-2
    [6] BALAKRISHNAN G, ZHAO A, SABUNCU M R, et al. An unsupervised learning model for deformable medical image registration[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 9252-9260.
    [7] HU Y, MODAT M, GIBSON E, et al. Weakly-supervised convolutional neural networks for multimodal image registration[J]. Medical Image Analysis, 2018, 49: 1-13. doi: 10.1016/j.media.2018.07.002
    [8] QIN C, SHI B, LIAO R, et al. Unsupervised deformable registration for multi-modal images via disentangled representations[C]//International Conference on Information Processing in Medical Imaging. Berlin: Springer, 2019: 249-261.
    [9] MAHAPATRA D, GE Z. Training data independent image registration using generative adversarial networks and domain adaptation[EB/OL]. [2020-07-21]. https://doi.org/10./olb/j.patcog.2019.107109.
    [10] ZHANG Y X, LIU R S, LI Z, et al. Coupling principled refinement with bi-directional deep estimation for robust deformable 3D medical image registration[C]//Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging. Piscataway: IEEE Press, 2020: 86-90.
    [11] LIU R S, LI Z, ZHANG Y X, et al. Bi-level probabilistic feature learning for deformable image registration[C]//International Joint Conference on Artificial Intelligence, 2020: 723-730.
    [12] JADERBERG M, SIMONYAN K, KAVUKCUOGLU K, et al. Spatial transformer networks[C]//Conference and Workshop on Neural Information Processing Systems. New York: Curran Associates, 2015: 2017-2025.
    [13] SUN D, YANG X, LIU M, et al. PWC-NET: CNNs for optical flow using pyramid, warping, and cost volume[C]//IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE Press, 2018: 8934-8943.
    [14] ZACH C, POCK T, BISCHOF H. A duality based approach for real TV-L1 optical flow[J]. Pattern Recognition, 2007, 4713: 214-223. http://www.springerlink.com/content/u6288280v55w0135
    [15] 徐峻岭, 周毓明, 陈林, 等. 基于互信息的无监督特征选择[J]. 计算机研究与发展, 2012, 49(2): 158-168. https://www.cnki.com.cn/Article/CJFDTOTAL-JFYZ201202022.htm

    XU J L, ZHOU Y M, CHEN L, et al. An unsupervised feature selection approach based on mutual information[J]. Journal of Computer Research and Development, 2012, 49(2): 158-168(in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-JFYZ201202022.htm
    [16] MAHAPATRA D, ANTONY B, SEDAI S. Deformable medical image registration using generative adversarial networks[C]//Proceedings of the 2018 IEEE 15th International Symposium on Biomedical Imaging. Piscataway: IEEE Press, 2018: 1449-1453.
    [17] MUELLER S G, WEINER M W, THAL L J, et al. Ways toward an early diagnosis in Alzheimer's disease: The Alzheimer's disease neuroimaging initiative(ADNI)[J]. Alzheimer's & Dementia, 2005, 1(1): 55-66. http://www.researchgate.net/publication/6354499_Mueller_SG_et_al_Ways_toward_an_early_diagnosis_in_Alzheimer's_disease_the_Alzheimer's_Disease_Neuroimaging_Initiative_ADNI_Alzheimers_Dement_1_55-66
    [18] KISER K J, AHMED S, STIEB S M, et al. Data from the thoracic volume and pleural effusion segmentations in diseased lungs for benchmarking chest ct processing pipelines[EB/OL]. [2020-08-22]. https://doi.org/10.7937/tcia.2020.6c7y-gq39.
    [19] AERTS H J W L, WEE L, VELAZQUEZ E R, et al. Data from NSCLC-radiomics[EB/OL]. [2020-08-22]. https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI.
    [20] CHILAMKURTHY S, GHOSH R, TANAMALA S, et al. Deep learning algorithms for detection of critical findings in head CT scans: A retrospective study[J]. Lancet, 2018, 392(10162): 2388-2396. doi: 10.1016/S0140-6736(18)31645-3
    [21] MA J, GE C, WANG Y, et al. COVID-19 CT lung and infection segmentation dataset(Version1.0)[EB/OL]. [2020-08-22]. http://doi.org/10.5281/zenodo.3757476.
    [22] AVANTS B B, TUSTISON N, GANG S. Advanced normalization tools (ANTs)[J]. Insight, 2009, 2(365): 1-35.
    [23] AVANTS B B, EPSTEIN C L, GROSSMAN M, et al. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain[J]. Medical Image Analysis, 2008, 12(1): 26-41. doi: 10.1016/j.media.2007.06.004
    [24] KLEIN S, STARING M, MURPHY K, et al. Elastix: A toolbox for intensity based medical image registration[J]. IEEE Transactions on Medical Imaging, 2010, 29(1): 196-205. doi: 10.1109/TMI.2009.2035616
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出版历程
  • 收稿日期:  2020-08-24
  • 录用日期:  2020-09-11
  • 刊出日期:  2021-03-20

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