2D-3D Medical Image Registration Based on Training-Inference Decoupling Architecture

Wenjü Li; Deqing Kong; Guogang Cao; Sicheng Li; Cuixia Dai

doi:10.3788/LOP202259.1610015

Journals >Laser & Optoelectronics Progress >Volume 59 >Issue 16 >Page 1610015 > Article

Laser & Optoelectronics Progress
Vol. 59, Issue 16, 1610015 (2022)

2D-3D Medical Image Registration Based on Training-Inference Decoupling Architecture

Wenjü Li¹, Deqing Kong¹, Guogang Cao^1、*, Sicheng Li¹, and Cuixia Dai²

Author Affiliations

¹School of Computer Science & Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China

²School of Sciences, Shanghai Institute of Technology, Shanghai 201418, China

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DOI: 10.3788/LOP202259.1610015 Cite this Article Set citation alerts

Wenjü Li, Deqing Kong, Guogang Cao, Sicheng Li, Cuixia Dai. 2D-3D Medical Image Registration Based on Training-Inference Decoupling Architecture[J]. Laser & Optoelectronics Progress, 2022, 59(16): 1610015 Copy Citation Text

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Fig. 1. Registration framework

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Fig. 2. Transform parameter renderings

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Fig. 3. Network structure of regressor

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Fig. 4. Re-parameterization process

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Fig. 5. Activate function. (a) ReLU/Swish; (b) ACON-C under different parameters

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Fig. 6. Parameter error box diagrams. (a) Translation error; (b) angular error

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Fig. 7. Registration rendering. (a) Reference image; (b) moving image; (c) registrated checkerboard rendering

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Parameter	Distribution range
t_x /mm	U（-20， 20）
t_y /mm	U（-20， 20）
t_z /mm	U（-10， 10）
t_θ /（°）	U（-10， 10）
t_α /（°）	U（-10， 10）
t_β /（°）	U（-5， 5）

Table 1. Transformation parameter distribution

Structure	Number of images /10⁴
Structure	Training set	Test set	Data set
Pelvis	3	1	4
Chest	3	1	4

Table 2. Number distribution of the datasets

Batchsize	T-MAE /mm	R-MAE /（°）	RMSE
8	0.08	0.05	0.08
16	0.10	0.06	0.09
32	0.15	0.12	0.12

Table 3. Comparison of network hyperparameters

Dataset	Network structure	T-MAE /mm	R-MAE /（°）	RMSE
Pelvis	No branch	0.103	0.073	0.115
	Identity	0.087	0.062	0.098
	1×1 Conv	0.083	0.057	0.093
	Proposed method	0.075	0.051	0.084
Chest	No branch	0.155	0.099	0.169
	Identity	0.147	0.088	0.156
	1×1 Conv	0.129	0.082	0.143
	Proposed method	0.115	0.068	0.125

Table 4. Error comparison of branch structure ablation experiments

Network structure	Parameter /10⁶	T-time /ms	I-time /ms
No branch	50.98	29.8	25.7
Identity	50.98	30.2	25.8
1×1 Conv	56.53	33.2	25.8
Proposed method	56.53	34.6	26.0

Table 5. Time comparison of branch structure ablation experiments

Dataset	Activation function	T-MAE /mm	R-MAE /（°）	RMSE
Pelvis	ReLU	0.078	0.052	0.087
	Swish	0.079	0.053	0.088
	ACON-C	0.078	0.052	0.087
	Meta-ACON	0.075	0.051	0.084
Chest	ReLU	0.143	0.084	0.156
	Swish	0.151	0.082	0.162
	ACON-C	0.129	0.077	0.129
	Meta-ACON	0.115	0.068	0.125

Table 6. Activation function comparison

Dataset	Network structure	T-MAE	R-MAE	RMSE	Time /ms
Pelvis	GoogLeNet	0.36±0.19	0.23±0.13	0.39±0.24	16.0
	VGG16	0.32±0.27	0.21±0.18	0.35±0.21	19.2
	ResNet50	0.09±0.08	0.06±0.07	0.10±0.08	18.4
	DenseNet121	0.11±0.08	0.07±0.06	0.12±0.09	34.5
	Net+GradNCC^［11］	7.83±19.8	4.94±8.78
	Proposed method	0.08±0.04	0.05±0.03	0.08±0.06	26.0
Chest	GoogLeNet	0.41±0.18	0.16±0.09	0.39±0.20	21.5
	VGG16	0.56±0.45	0.31±0.24	0.60±0.41	19.1
	ResNet50	0.33±0.09	0.20±0.05	0.35±0.12	24.6
	DenseNet121	0.15±0.08	0.09±0.05	0.16±0.08	37.0
	Inception+branch^［10］	0.61	1.93		2.3
	Proposed method	0.12±0.07	0.07±0.04	0.13±0.08	26.4

Table 7. Comparison of registration effects of different networks

Dataset	Method	NCC	NMI	SSIM	Time /ms
Pelvis	NCC_Powell	0.56±0.07	0.16±0.02	0.57±0.09	96.4×10³
	NMI_Powell	0.61±0.09	0.15±0.01	0.57±0.09	28.6×10³
	Deep learning method^［9］	0.82±0.07	0.32±0.03		30.0
	Proposed method	0.99±0.05	0.72±0.03	0.99±0.08	26.0
Chest	NCC_Powell	0.53±0.11	0.11±0.02	0.55±0.09	101.5×10³
	NMI_Powell	0.57±0.09	0.11±0.02	0.58±0.08	29.2×10³
	Inception+branch^［10］			0.42	2.3×10³
	Proposed method	0.99±0.06	0.56±0.02	0.99±0.09	26.4

Table 8. Comparison of registration methods

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