Multimodal deformable registration based on unsupervised learning

被引：0
|
作者：
Ma T. [1 ]
Li Z. [1 ]
Liu R. [1 ]
Fan X. [1 ]
Luo Z. [1 ]
机构：
[1] International School of Information Science & Engineering, Dalian University of Technology, Dalian
来源：
Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics | 2021年 / 47卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Computer vision; Deep learning; Medical image registration; Multimodal; Unsupervised;
D O I：
10.13700/j.bh.1001-5965.2020.0449
中图分类号：
学科分类号：
摘要：
Multimodal deformable registration is designed to solve dense spatial transformations and is used to align images of two different modalities. It is a key issue in many medical image analysis applications. Multimodal image registration based on traditional methods aims to solve the optimization problem of each pair of images, and usually achieves excellent registration performance, but the calculation cost is high and the running time is long. The deep learning method greatly reduces the running time by learning the network used to perform registration. These learning-based methods are very effective for single-modality registration. However, the intensity distribution of different modal images is unknown and complex. Most existing methods rely heavily on label data. Faced with these challenges, this paper proposes a deep multimodal registration framework based on unsupervised learning. Specifically, the framework consists of feature learning based on matching amount and deformation field learning based on maximum posterior probability, and realizes unsupervised training by means of spatial conversion function and differentiable mutual information loss function. In the 3D image registration tasks of MRI T1, MRI T2 and CT, the proposed method is compared with the existing advanced multi-modal registration methods. In addition, the registration performance of the proposed method is demonstrated on the latest COVID-19 CT data. A large number of results show that the proposed method has a competitive advantage in registration accuracy compared with other methods, and greatly reduces the calculation time. © 2021, Editorial Board of JBUAA. All right reserved.
引用
收藏
页码：658 / 664
页数：6
相关论文
共 24 条
  • [1] MAINTZ J B A, VIERGEVER M A., A survey of medical image registration, Medical Image Analysis, 2, 1, pp. 1-36, (1998)
  • [2] ASHBURNER J., A fast diffeomorphic image registration algorithm, NeuroImage, 38, 1, pp. 95-113, (2007)
  • [3] GUIZAR-SICAIROS M, THURMAN S T, FIENUP J R., Efficient subpixel image registration algorithms, Optics Letters, 33, 2, pp. 156-158, (2008)
  • [4] MAES F, VANDERMEULEN D, SUETENS P., Medical image registration using mutual information, Proceedings of the IEEE, 10, 91, pp. 1699-1722, (2003)
  • [5] D'AGOSTINO E, MAES F, VANDERMEULEN D, Et al., A viscous fluid model for multimodal non-rigid image registration using mutual information, Medical Image Analysis, 7, 4, pp. 565-575, (2003)
  • [6] BALAKRISHNAN G, ZHAO A, SABUNCU M R, Et al., An unsupervised learning model for deformable medical image registration, IEEE Conference on Computer Vision and Pattern Recognition, pp. 9252-9260, (2018)
  • [7] HU Y, MODAT M, GIBSON E, Et al., Weakly-supervised convolutional neural networks for multimodal image registration, Medical Image Analysis, 49, pp. 1-13, (2018)
  • [8] QIN C, SHI B, LIAO R, Et al., Unsupervised deformable registration for multi-modal images via disentangled representations, International Conference on Information Processing in Medical Imaging, pp. 249-261, (2019)
  • [9] MAHAPATRA D, GE Z., Training data independent image registration using generative adversarial networks and domain adaptation
  • [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, Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging, pp. 86-90, (2020)
123