Groupwise Registration for Magnetic Resonance Image Based on Variational Inference

doi:10.11938/cjmr20212918

Abstract

Abstract:

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.

Key words: deep learning, groupwise registration, variational inference, deformable registration

CLC Number:

O482.53

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.

Figures/Tables 11

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方法	Avg. Dice^*	GPU运行时间/s^*	Percentages (%) of ${(\det ({J_\phi }(p)) \leqslant 0)^*}$	EM₁	EM₂
ANTs (Syn)	0.689 (0.036)		45.6 (19)	930.98	6.54
voxelmorph-diff	0.698 (0.030)	0.51 (0.03)	49.6 (11)	960.67	6.29
voxelmorph	0.672 (0.031)	0.49 (0.02)	48.9 (26)	969.46	7.08
本文方法	0.701 (0.025)	0.40 (0.02)	45.0 (18)	940.89	5.07

噪声方差（均值为0）	Avg. Dice^*	GPU运行时间/s^*	Percentages (%) of ${(\det ({J_\phi }(p)) \leqslant 0)^*}$	EM₁	EM₂
0.001	0.686 (0.026)	0.50 (0.02)	48.8 (21)	1330.97	89.54
0.002	0.683 (0.030)	0.60 (0.04)	49.3 (27)	1360.67	100.29
0.003	0.646 (0.031)	0.58 (0.02)	51.7 (19)	1469.46	99.08
0.004	0.656 (0.020)	0.62 (0.03)	54.5 (28)	1480.89	113.07

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