Texture filtering based unsupervised registration methods and its application in liver computed tomography images_Journal of Biomedical Engineering

Authors：

WANG Peng ^1,2 , YAN Yunqi ³ , QIAN Lijun ³ , SUO Shiteng ³ ,  GUO Yi ^1,2 , XU Jianrong ³ ,  WANG Yuanyuan ^1,2

1. Department of Electronic Engineering, Fudan University, Shanghai 200433, P.R.China;
2. Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai 200433, P.R.China;
3. Department of Radiology, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Shanghai 200127, P.R.China;

Corresponding author：

GUO Yi, Email: guoyi@fudan.edu.cn; WANG Yuanyuan, Email: yywang@fudan.edu.cn

Keywords：

liver computed tomography images; unsupervised registration; texture filtering; cascaded network; texture similarity

DOI：

10.7507/1001-5515.202102001

Video：

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Abstract Full text Figures/Tables Video References Cited by

Image registration is of great clinical importance in computer aided diagnosis and surgical planning of liver diseases. Deep learning-based registration methods endow liver computed tomography (CT) image registration with characteristics of real-time and high accuracy. However, existing methods in registering images with large displacement and deformation are faced with the challenge of the texture information variation of the registered image, resulting in subsequent erroneous image processing and clinical diagnosis. To this end, a novel unsupervised registration method based on the texture filtering is proposed in this paper to realize liver CT image registration. Firstly, the texture filtering algorithm based on L0 gradient minimization eliminates the texture information of liver surface in CT images, so that the registration process can only refer to the spatial structure information of two images for registration, thus solving the problem of texture variation. Then, we adopt the cascaded network to register images with large displacement and large deformation, and progressively align the fixed image with the moving one in the spatial structure. In addition, a new registration metric, the histogram correlation coefficient, is proposed to measure the degree of texture variation after registration. Experimental results show that our proposed method achieves high registration accuracy, effectively solves the problem of texture variation in the cascaded network, and improves the registration performance in terms of spatial structure correspondence and anti-folding capability. Therefore, our method helps to improve the performance of medical image registration, and make the registration safely and reliably applied in the computer-aided diagnosis and surgical planning of liver diseases.

Citation： WANG Peng, YAN Yunqi, QIAN Lijun, SUO Shiteng, GUO Yi, XU Jianrong, WANG Yuanyuan. Texture filtering based unsupervised registration methods and its application in liver computed tomography images. Journal of Biomedical Engineering, 2021, 38(5): 819-827. doi: 10.7507/1001-5515.202102001 Copy

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16.	Jaderberg M, Simonyan K, Zisserman A. Spatial transformer networks. Advances in Neural Information Processing Systems. 2015: 2017-2025.
17.	Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation// International Conference on Medical Image Computing and Computer-assisted Intervention. Springer Cham, 2015: 234-241.
18.	Kingma D P, Ba J. Adam: a method for stochastic optimization. arXiv preprint, 2017, arXiv: 1412.6980.

1. Schregel K, Tsogkas I, Peter C, et al. Outcome prediction using perfusion parameters and collateral scores of multi-phase and single-phase CT angiography in acute stroke: need for one, two, three, or thirty scans?. J Stroke, 2018, 20(3): 362-372.
2. Grabherr S, Doenz F, Steger B, et al. Multi-phase post-mortem CT angiography: development of a standardized protocol. Int J Legal Med, 2011, 125(6): 791-802.
3. Brenner R, Metens T, Bali M, et al. Pancreatic neuroendocrine tumor: added value of fusion of T2-weighted imaging and high b-value diffusion-weighted imaging for tumor detection. European Journal of Radiology, 2012, 81(5): e746-e749.
4. Fu Y, Liu S, Li H, et al. Automatic and hierarchical segmentation of the human skeleton in CT images. Phys Med Biol, 2017, 62(7): 2812-2833.
5. El-Gamal F A. Elmogy M, Atwan A. Current trends in medical image registration and fusion. Egyptian Informatics Journal, 2016, 17(1): 99-124.
6. Aljabar P, Wolz R, Rueckert D. Manifold learning for medical image registration, segmentation, and classification. Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis, 2012: 351-372.
7. Klein S, Staring M, Murphy K, et al. Elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging, 2010, 29(1): 196-205.
8. Avants B, Tustison N, Song G. Advanced normalization tools(ANTS). Insight Journal, 2009, 2(365): 1-35.
9. Balakrishnan G, Zhao A, Sabuncu M R, et al. An unsupervised learning model for deformable medical image registration//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City: IEEE, 2018: 9252-9260.
10. Zhang J. Inverse-consistent deep networks for unsupervised deformable image registration. arXiv preprint, 2018, arXiv: 1809.03443.
11. Balakrishnan G, Zhao A, Sabuncu M R, et al. VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans Med Imaging, 2019, 38(8): 1788-1800.
12. Lau T, Luo J, Zhao S, et al. Unsupervised 3D end-to-end medical image registration with volume tweening network. arXiv preprint, 2019, arXiv: 1902.05020.
13. Zhao Shengyu, Dong Yue, Chang E, et al. Recursive cascaded networks for unsupervised medical image registration//2019 IEEE/CVF International Conference on Computer Vision (ICCV), 2019: 10599-10609.
14. de Vos B D, Berendsen F F, Viergever M A, et al. A deep learning framework for unsupervised affine and deformable image registration. Med Image Anal, 2019, 52: 128-143.
15. Xu L, Lu C, Xu Y, et al. Image smoothing via L0 gradient minimization. Association for Computing Machinery Transactions on Graphics, 2011, 30(6): 174-186.
16. Jaderberg M, Simonyan K, Zisserman A. Spatial transformer networks. Advances in Neural Information Processing Systems. 2015: 2017-2025.
17. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation// International Conference on Medical Image Computing and Computer-assisted Intervention. Springer Cham, 2015: 234-241.
18. Kingma D P, Ba J. Adam: a method for stochastic optimization. arXiv preprint, 2017, arXiv: 1412.6980.

Journal of Biomedical Engineering

Texture filtering based unsupervised registration methods and its application in liver computed tomography images

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