Chinese Journal of Magnetic Resonance >
Groupwise Registration for Magnetic Resonance Image Based on Variational Inference
Received date: 2021-05-13
Online published: 2021-08-27
To address the low precision of pairwise registration method based on the deep learning and the time-consuming nature of traditional registration algorithm, this paper presents a method of unsupervised end-to-end groupwise registration based on variational inference, as well as a registration framework based on normalized cross correlation (NCC) and prior knowledge. The framework can warp all images in the group into a common space and effectively control the deformation field of the regularization, and it doesn't need a real deformation field or a reference image. The estimation of deformation field by this method can be modeled as a probability generation model and solved by variational inference. Then unsupervised training is implemented with the help of spatial transformer network and loss function. The registration results of 3D brain magnetic resonance image from the public data set LPBA40 show that: compared with the baseline method, the proposed method has better Dice score, less running time, better diffeomorphisms domain, and is robust to noise.
Qin ZHOU , Yuan-jun WANG . Groupwise Registration for Magnetic Resonance Image Based on Variational Inference[J]. Chinese Journal of Magnetic Resonance, 2022 , 39(3) : 291 -302 . DOI: 10.11938/cjmr20212918
| 1 | WANG Y, JIANG F, LIU YReference-free brain template construction with population symmetric registration[J]. Med Biol Eng Comput, 2020,58 (9): 2083- 2093. |
| 2 | MARTíN-GONZáLEZ E, SEVILLA T, REVILLA-ORODEA A, et alGroupwise non-rigid registration with deep learning: an affordable solution applied to 2D cardiac cine MRI reconstruction[J]. Entropy, 2020,22 (6): 687. |
| 3 | GEE J C, REIVICH M, BILANIUK L, et alEvaluation of multiresolution elastic matching using mri data[J]. Proc Spie, 1991,1445,226- 234. |
| 4 | RUECKERT D, SONODA L INonrigid registration using free-form deformations: application to breast mr images[J]. IEEE T Med Imaging, 1999,18 (8): 712- 721. |
| 5 | THIRION J PImage matching as a diffusion process: an analogy with Maxwell's demons[J]. Med Image Anal, 2011,2 (3): 243- 260. |
| 6 | ZHONG J, PHUA D, QIU AQuantitative evaluation of lddmm, freesurfer, and caret for cortical surface mapping[J]. Neuroimage, 2010,52 (1): 131- 141. |
| 7 | ASAMI T, BOUIX S, WHITFORD T J, et alLongitudinal loss of gray matter volume in patients with first-episode schizophrenia: dartel automated analysis and roi validation[J]. Neuroimage, 2012,59 (2): 986- 996. |
| 8 | GUIMOND A, MEUNIER J, THIRION J PAverage brain models: a convergence study[J]. Comput Vis Image Und, 2000,77 (2): 192- 210. |
| 9 | SEGHERS D, D'AGOSTINO E, MAES F, et al. Construction of a brain template from mr images using state-of-the-art registration and segmentation techniques[C]// Medical Image Computing and Computer-Assisted Intervention--MICCAI 2004, 7th International Conference Saint-Malo, France, September 26-29, 2004, Proceedings, Part I. 2004. |
| 10 | WU G, JIA H, WANG Q, et alSharpmean: groupwise registration guided by sharp mean image and tree-based registration[J]. Neuroimage, 2011,56 (4): 1968- 1981. |
| 11 | WU G, WANG Q, JIA H, et alFeature-based groupwise registration by hierarchical anatomical correspondence detection[J]. Hum Brain Mapp, 2012,33 (2): 253- 271. |
| 12 | YANOVSKY I, THOMPSON P M, OSHER S, et al. Topology preserving log-unbiased nonlinear image registration: theory and implementation[C]// IEEE Conference on Computer Vision & Pattern Recognition. Minneapolis, Minnesota, USA: IEEE, 2007. |
| 13 | WANG Q, CHEN L Y YAP P T, et alGroupwise registration based on hierarchical image clustering and atlas synthesis[J]. Hum Brain Mapp, 2010,31,1128- 1140. |
| 14 | CHE T, ZHENG Y, CONG J, et alDeep group-wise registration for multi-spectural images from fundus images[J]. IEEE Access, 2019,7,27650- 27661. |
| 15 | CHE T, ZHENG Y, SUI X, et al. Dgr-net: deep groupwise registration of multispectral images[C]// Information Processing in Medical Imaging - 26th International Conference , Hong Kong, china: IPMI, 2019: 706-717. |
| 16 | HAASE R, HELDMANN S, LELLMANN J. Deformable groupwise image registration using low-rank and sparse decomposition[EB/OL]. [2020-06-10].https://arxiv.org/abs/2001.03509. |
| 17 | DALCA A V, RAKIC M, GUTTAG J, et al. Learning conditional deformable templates with convolutional networks[C]//Neural Information Processing Systems 2019(NeurIPS 2019), Vancouver, BC, Canada, 2019: 804-816. |
| 18 | YU E M, DALCA A V, SABUNCU M R Learning conditional deformable shape templates for brain anatomy[M]. Lima: Machine Learning in Medical Imaging, 2020, 353- 352. |
| 19 | SIEBERT H, HEINRICH M P Deep groupwise registration of mri using deforming autoencoders[M]. Berlin: Springer, 2020, 236- 241. |
| 20 | HE Z Y, CHUNG A C S. Unsupervised end-to-end groupwise registration framework without generating templates[C]// 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020: 375-379. |
| 21 | DALCA A V, BALAKRISHNAN G, GUTTAG J, et alUnsupervised learning of probabilistic diffeomorphic registration for images and surfaces[J]. Med Image Anal, 2019,57,226- 236. |
| 22 | BALAKRISHNAN G, ZHAO A, SABUNCU M R, et alVoxelmorph: a learning framework for deformable medical image registration[J]. IEEE T Med Imaging, 2019,1788- 1800. |
| 23 | SHATTUCK D W, MIRZA M, ADISETIYO V, et alConstruction of a 3d probabilistic atlas of human cortical structures.[J]. Neuroimage, 2008,39 (3): 1064- 1080. |
| 24 | 王远军, 刘玉基于图像集拓扑中心的群体配准方法[J]. 波谱学杂志, 2018,35 (4): 60- 67. |
| 24 | WANG Y J, LIU YGroup registration method based on topological center of images[J]. Chinese J Magn Reson, 2018,35 (4): 60- 67. |
| 25 | 蔡文琴, 王远军基于磁共振成像的人脑图谱构建方法研究进展[J]. 波谱学杂志, 2020,37 (2): 241- 253. |
| 25 | CAI W Q, WANG Y JAdvances in the construction of the human brain map based on magnetic resonance imaging[J]. Chinese J Magn Reson, 2020,37 (2): 241- 253. |
| 26 | 刘可文, 刘紫龙, 汪香玉, 等基于级联卷积神经网络的前列腺磁共振图像分类[J]. 波谱学杂志, 2020,37 (2): 152- 161. |
| 26 | LIU K W, LIU Z L, WANG X Y, et alProstate magnetic resonance image classification based on cascading convolutional neural networks[J]. Chinese J Magn Reso, 2020,37 (2): 152- 161. |
/
| 〈 |
|
〉 |