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Recent Advances in Computer Science and Communications

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ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

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Research Article

Broad-UNet-diff: Diffeomorphic Deformable Medical Image Registration based on Multi-scale Feature Learning

Author(s): Tianqi Cheng, Lei Wang*, Yuwei Wang, Xinping Guo and ChunXiang Liu

Volume 17, Issue 3, 2024

Published on: 24 October, 2023

Article ID: e241023222582 Pages: 10

DOI: 10.2174/0126662558257094231018062232

Price: $65

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Abstract

Introduction: To propose a medical image registration method with significant performance improvement. The spatial transformation obtained by the traditional deformable image registration technology is not smooth enough, and the calculation amount is too large to solve the optimization parameters. The network model proposed based on deep learning medical image registration technology has some limitations, which cannot guarantee the registration of topological structures, resulting in the loss of spatial features. It makes the model have topological conservation and transform reversibility, has the ability to learn more multi-scale features and complex image structures, and captures finer changes while clearly encoding global correlation.

Method: Based on the core UNet model, a deformable image registration method with a new architecture Broad-UNet-diff is proposed. The model is equipped with asymmetric parallel convolution and uses diffeomorphism mapping.

Result: Compared with the seven classical registration methods under the brain MRI datasets, the proposed method has significantly improved the registration performance. In particular, compared with the advanced TransMorph-diff registration method, the Dice score can be improved by 12 %, but only the 1/10 parameters are needed.

Conclusion: This method confirms its convincing effectiveness and accuracy.

Graphical Abstract

[1]
M. Ginesi, D. Meli, A. Roberti, N. Sansonetto, and P. Fiorini, "Dynamic movement primitives: Volumetric obstacle avoidance using dynamic potential functions", J. Intell. Robot. Syst., vol. 101, no. 4, p. 79, 2021.
[http://dx.doi.org/10.1007/s10846-021-01344-y]
[2]
X. Liu, Z. Li, M. Ishii, G.D. Hager, R.H. Taylor, and M. Unberath, "Sage: Slam with appearance and geometry prior for endoscopy", In 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA, 2022, pp. 5587-5593
[http://dx.doi.org/10.1109/ICRA46639.2022.9812257]
[3]
G. Gaziv, R. Beliy, N. Granot, A. Hoogi, F. Strappini, T. Golan, and M. Irani, "Self-supervised natural image reconstruction and large-scale semantic classification from brain activity", Neuroimage, vol. 254, p. 119121, 2022.
[http://dx.doi.org/10.1016/j.neuroimage.2022.119121] [PMID: 35342004]
[4]
S. Guo, X. Yang, J. Ma, G. Ren, and L. Zhang, "A differentiable two-stage alignment scheme for burst image reconstruction with large shift", In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022, pp. 17472-17481
[http://dx.doi.org/10.1109/CVPR52688.2022.01695]
[5]
Y. Xie, J. Zhang, C. Shen, and Y. Xia, "Cotr: Efficiently bridging cnn and transformer for 3d medical image segmentation", In: M. de Bruijne, Ed., Medical Image Computing and Computer Assisted Intervention : MICCAI 2021, vol. 12903. Springer: Cham, 2021, pp. 171-180.
Lecture Notes in Computer Science171–180 [http://dx.doi.org/10.1007/978-3-030-87199-4_16]
[6]
Z. Shen, Z. Xu, S. Olut, and M. Niethammer, "Anatomical data augmentation via fluid-based image registration", In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8 , 2020.
Proceedings, Part III (pp.318-328),2020. [http://dx.doi.org/10.1007/978-3-030-59716-0_31]
[7]
Dinggang Shen, and C. Davatzikos, "HAMMER: Hierarchical attribute matching mechanism for elastic registration", IEEE Trans. Med. Imaging, vol. 21, no. 11, pp. 1421-1439, 2002.
[http://dx.doi.org/10.1109/TMI.2002.803111] [PMID: 12575879]
[8]
I. Arganda-Carreras, C.O. Sorzano, R. Marabini, J.M. Carazo, C. Ortiz-de-Solorzano, and J. Kybic, "Consistent and elastic registration of histological sections using vector-spline regularization", In: R.R. Beichel, and M. Sonka, Eds., Computer Vision Approaches to Medical Image Analysis. CVAMIA 2006, vol. 4241. Springer: Berlin, Heidelberg, 2006, pp. 85-95.
Lecture Notes in Computer Science [http://dx.doi.org/10.1007/11889762_8]
[9]
A.V. Dalca, G. Balakrishnan, J. Guttag, and M.R. Sabuncu, "Unsupervised learning for fast probabilistic diffeomorphic registration", In International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 2018: Medical Image Computing and Computer Assisted Intervention : MICCAI, 2018, pp. 729-738
[http://dx.doi.org/10.1007/978-3-030-00928-1_82]
[10]
A.V. Dalca, G. Balakrishnan, J. Guttag, and M.R. Sabuncu, "Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces", Med. Image Anal., vol. 57, pp. 226-236, 2019.
[http://dx.doi.org/10.1016/j.media.2019.07.006] [PMID: 31351389]
[11]
J. Zhang, J. Wang, X. Wang, and D. Feng, "The adaptive FEM elastic model for medical image registration", Phys. Med. Biol., vol. 59, no. 1, pp. 97-118, 2014.
[http://dx.doi.org/10.1088/0031-9155/59/1/97] [PMID: 24334618]
[12]
W. Zhang, and Y. Zhao, "Hierarchical registration of brain images based on B-splines and Laplacian commutators", Optik, vol. 241, p. 167022, 2021.
[http://dx.doi.org/10.1016/j.ijleo.2021.167022]
[13]
Y. Zhang, Y. Pei, and H. Zha, "Learning dual transformer network for diffeomorphic registration", In International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 2021: Medical Image Computing and Computer Assisted Intervention : MICCAI, 2021, pp. 129-138
[http://dx.doi.org/10.1007/978-3-030-87202-1_13]
[14]
B. Avants, C. Epstein, M. Grossman, and J. Gee, "Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain", Med. Image Anal., vol. 12, no. 1, pp. 26-41, 2008.
[http://dx.doi.org/10.1016/j.media.2007.06.004] [PMID: 17659998]
[15]
M.F. Beg, M.I. Miller, A. Trouvé, and L. Younes, "Computing large deformation metric mappings via geodesic flows of diffeomorphisms", Int. J. Comput. Vis., vol. 61, no. 2, pp. 139-157, 2005.
[http://dx.doi.org/10.1023/B:VISI.0000043755.93987.aa]
[16]
B.D. de Vos, F.F. Berendsen, M.A. Viergever, H. Sokooti, M. Staring, and I. Išgum, "A deep learning framework for unsupervised affine and deformable image registration", Med. Image Anal., vol. 52, pp. 128-143, 2019.
[http://dx.doi.org/10.1016/j.media.2018.11.010] [PMID: 30579222]
[17]
K. Han, Y. Wang, H. Chen, X. Chen, J. Guo, Z. Liu, Y. Tang, A. Xiao, C. Xu, Y. Xu, Z. Yang, Y. Zhang, and D. Tao, "A survey on vision transformer", IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 1, pp. 87-110, 2023.
[http://dx.doi.org/10.1109/TPAMI.2022.3152247] [PMID: 35180075]
[18]
J. Chen, Y. He, E.C. Frey, Y. Li, and Y. Du, "Vit-v-net: Vision transformer for unsupervised volumetric medical image registration", arXiv:2104.06468, 2021.
[19]
O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation", In International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 2015: Medical Image Computing and Computer-Assisted Intervention : MICCAI, 2015, pp. 234-241
[http://dx.doi.org/10.1007/978-3-319-24574-4_28]
[20]
G. Balakrishnan, A. Zhao, M.R. Sabuncu, J. Guttag, and A.V. Dalca, "VoxelMorph: A learning framework for deformable medical image registration", IEEE Trans. Med. Imaging, vol. 38, no. 8, pp. 1788-1800, 2019.
[http://dx.doi.org/10.1109/TMI.2019.2897538] [PMID: 30716034]
[21]
G. Balakrishnan, A. Zhao, M.R. Sabuncu, J. Guttag, and A.V. Dalca, "An unsupervised learning model for deformable medical image registration",In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA,pp. 9252-9260,
2018. [http://dx.doi.org/10.1109/CVPR.2018.00964]
[22]
B.B. Avants, N.J. Tustison, G. Song, P.A. Cook, A. Klein, and J.C. Gee, "A reproducible evaluation of ANTs similarity metric performance in brain image registration", Neuroimage, vol. 54, no. 3, pp. 2033-2044, 2011.
[http://dx.doi.org/10.1016/j.neuroimage.2010.09.025] [PMID: 20851191]
[23]
J.M. Valverde, A. Shatillo, R. De Feo, and J. Tohka, "Automatic cerebral hemisphere segmentation in Rat MRI with ischemic lesions via attention-based convolutional neural networks", Neuroinformatics, vol. 21, no. 1, pp. 57-70, 2023.
[http://dx.doi.org/10.1007/s12021-022-09607-1] [PMID: 36178571]
[24]
Z. Shen,, X. Han,, Z. Xu,, and M. Niethammer,, "Networks for joint affine and non-parametric image registration",In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, USA pp. 4224-4233
2019 [http://dx.doi.org/10.1109/CVPR.2019.00435]
[25]
T.C. Mok,, and A. Chung, "Fast symmetric diffeomorphic image registration with convolutional neural networks",In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, virtually, pp. 4644-4653
2020 [http://dx.doi.org/10.1109/CVPR42600.2020.00470]
[26]
X. Jia, A. Thorley, W. Chen, H. Qiu, L. Shen, I.B. Styles, H.J. Chang, A. Leonardis, A. de Marvao, D.P. O’Regan, D. Rueckert, and J. Duan, "Learning a model-driven variational network for deformable image registration", IEEE Trans. Med. Imaging, vol. 41, no. 1, pp. 199-212, 2022.
[http://dx.doi.org/10.1109/TMI.2021.3108881] [PMID: 34460369]
[27]
J. Cheng, A.V. Dalca, B. Fischl, and L. Zöllei, "Cortical surface registration using unsupervised learning", Neuroimage, vol. 221, p. 117161, 2020.
[http://dx.doi.org/10.1016/j.neuroimage.2020.117161] [PMID: 32702486]
[28]
S. Sun, K. Han, D. Kong, H. Tang, X. Yan, and X. Xie, "Topology-preserving shape reconstruction and registration via neural diffeomorphic flow", In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA,pp. 20845-20855
2022 [http://dx.doi.org/10.1109/CVPR52688.2022.02018]
[29]
M. Brunn, N. Himthani, G. Biros, M. Mehl, and A. Mang, "Fast GPU 3D diffeomorphic image registration", J. Parallel Distrib. Comput., vol. 149, pp. 149-162, 2021.
[http://dx.doi.org/10.1016/j.jpdc.2020.11.006] [PMID: 33380769]
[30]
M. Brunn, N. Himthani, G. Biros, M. Mehl, and A. Mang, "Multi-node multi-GPU diffeomorphic image registration for large-scale imaging problems", In SC20: International Conference for High Performance Computing, Networking, Storage and Analysis, Atlanta, GA, USA, 2020, pp. 1-17
[http://dx.doi.org/10.1109/SC41405.2020.00042]
[31]
T.C. Mok, and A.C. Chung, "Large deformation diffeomorphic image registration with laplacian pyramid networks", In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8 , 2020.
Proceedings, Part III. [http://dx.doi.org/10.1007/978-3-030-59716-0_21]
[32]
J. Chen, E.C. Frey, Y. He, W.P. Segars, Y. Li, and Y. Du, "TransMorph: Transformer for unsupervised medical image registration", Med. Image Anal., vol. 82, p. 102615, 2022.
[http://dx.doi.org/10.1016/j.media.2022.102615] [PMID: 36156420]
[33]
M. Modat, G.R. Ridgway, Z.A. Taylor, M. Lehmann, J. Barnes, D.J. Hawkes, N.C. Fox, and S. Ourselin, "Fast free-form deformation using graphics processing units", Comput. Methods Programs Biomed., vol. 98, no. 3, pp. 278-284, 2010.
[http://dx.doi.org/10.1016/j.cmpb.2009.09.002] [PMID: 19818524]
[34]
B.B. Avants, N.J. Tustison, J. Wu, P.A. Cook, and J.C. Gee, "An open source multivariate framework for n-tissue segmentation with evaluation on public data", Neuroinformatics, vol. 9, no. 4, pp. 381-400, 2011.
[http://dx.doi.org/10.1007/s12021-011-9109-y] [PMID: 21373993]

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