Deformable Registration Network Based on Multi-scale Dilated Residual and Dual Attention

doi:10.11938/cjmr20253190

Abstract

Abstract:

Deformable registration plays a significant role in multiple medical image analysis tasks. However, the fixed receptive field of convolutional neural networks makes it difficult to fully capture the spatial context information of the brain. While the Transformer architecture can effectively capture global information, it suffers from high computational cost. To address this dilemma, we propose a deformable registration network based on multi-scale dilated residual convolution and dual attention. This network employs a multi-scale dilated residual convolution to capture local details and extensive context information simultaneously. A dual attention module is integrated to enhance feature representation capability. The decoder section introduces dynamic upsampling to precisely reconstruct high-frequency details, thereby ensuring the topological integrity of the deformation field. Experiments were conducted on two public brain datasets. The results demonstrate that the proposed model achieves high registration performance across multiple metrics, including Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and percentage of non-positive Jacobian determinant, indicating superior registration precision and smoother deformation fields.

Key words: deformable image registration, multi-scale, dual attention, dynamic upsampling

CLC Number:

O482.53

YANG Jingjing, WANG Yuanjun. Deformable Registration Network Based on Multi-scale Dilated Residual and Dual Attention[J]. Chinese Journal of Magnetic Resonance, 2026, 43(3): 291-306.

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Model	DSC	HD95/mm	%\|J_ϕ\|≤0/%	Params/k	Flops/G	Memory/MB	Time/s
SyN	0.641 ± 0.040	2.118 ± 0.754	<0.001	N/A	N/A	254	189.172
NiftyReg	0.635 ± 0.066	2.166 ± 0.655	0.006 ± 0.016	N/A	N/A	254	69.962
VoxelMorph-1	0.722 ± 0.029	2.287 ± 0.805	1.668 ± 0.345	274.39	305.32	9128	0.378
VoxelMorph-2	0.728 ± 0.030	2.278 ± 0.797	1.580 ± 0.344	301.41	400.31	10524	0.417
CycleMorph	0.738 ± 0.028	2.156 ± 0.760	1.732 ± 0.365	361.30	127.02	23724	0.483
Vit-V-Net	0.643 ± 0.041	2.399 ± 0.854	1.391 ± 0.284	31560.08	391.12	11370	0.317
TransMorph	0.744 ± 0.031	2.273 ± 0.786	1.540 ± 0.335	46771.25	714.16	16840	0.450
MDDA-Net （本文模型）	0.751 ± 0.027	2.107 ± 0.765	1.384 ± 0.355	439.18	581.96	16572	0.364

Model	DSC	HD95/mm	%\|J_ϕ\|≤0/%	Params/k	Flops/G	Memory/MB	Time/s
SyN	0.670 ± 0.032	1.105 ± 0.180	<0.001	N/A	N/A	204	108.688
NiftyReg	0.660 ± 0.018	1.312 ± 0.401	<0.001	N/A	N/A	204	32.996
VoxelMorph-1	0.650 ± 0.027	1.310 ± 0.399	0.717 ± 0.302	274.39	218.09	8080	0.207
VoxelMorph-2	0.651 ± 0.029	1.143 ± 0.261	0.665 ± 0.285	301.41	285.94	9804	0.242
CycleMorph	0.670 ± 0.026	1.143 ± 0.261	0.625 ± 0.236	361.30	90.73	17192	0.221
Vit-V-Net	0.636 ± 0.029	1.258 ± 0.409	0.586 ± 0.268	31560.08	279.31	8044	0.225
TransMorph	0.668 ± 0.027	1.177 ± 0.339	0.630 ± 0.240	46771.25	511.97	11978	0.257
MDDA-Net （本文模型）	0.673 ± 0.023	1.104 ± 0.179	0.507 ± 0.139	439.18	415.68	11954	0.237

MDRC	CSDA	Dysample	DSC	HD95/mm	%\|J_ϕ\|≤0/%	Params/k	Flops/G	Memory/MB	Time/s
			0.730 ± 0.030	2.146 ± 0.809	1.645 ± 0.355	396.55	571.33	11052	0.213
√			0.737 ± 0.028	2.305 ± 0.843	1.567 ± 0.349	413.41	575.03	13230	0.212
	√		0.734 ± 0.029	2.197 ± 0.789	1.565 ± 0.348	397.36	572.62	12558	0.208
		√	0.745 ± 0.027	2.201 ± 0.786	1.533 ± 0.332	421.51	576.96	12910	0.211
√	√		0.739 ± 0.029	2.162 ± 0.793	1.496 ± 0.344	414.22	576.33	14732	0.216
√		√	0.749 ± 0.026	2.134 ± 0.771	1.383 ± 0.328	438.37	580.66	15068	0.339
	√	√	0.748 ± 0.028	2.131 ± 0.767	1.523 ± 0.343	422.32	578.25	14298	0.235
√	√	√	0.751 ± 0.027	2.107 ± 0.765	1.384 ± 0.355	439.18	581.96	16572	0.364

Model	DSC	HD95/mm	%\|J_ϕ\|≤0/%	Params/k	Flops/G	Memory/MB	Time/s(GPU)
+ASPP	0.751 ± 0.028	2.124 ± 0.782	1.506 ± 0.352	466.05	591.62	18978	0.374
+MDRC	0.751 ± 0.027	2.107 ± 0.765	1.384 ± 0.355	439.18	581.96	16572	0.364
+CBAM	0.745 ± 0.026	2.170 ± 0.826	1.448 ± 0.344	439.00	581.65	16310	0.377
+SE	0.747 ± 0.026	2.151 ± 0.834	1.394 ± 0.328	438.79	580.95	15630	0.344
+CSDA	0.751 ± 0.027	2.107 ± 0.765	1.384 ± 0.355	439.18	581.96	16572	0.364
+nearest	0.739 ± 0.029	2.162 ± 0.793	1.496 ± 0.344	414.22	576.33	14732	0.216
+trilinear	0.740 ± 0.023	2.126 ± 0.766	1.434 ± 0.379	414.22	576.33	14732	0.351
+Dysample	0.751 ± 0.027	2.107 ± 0.765	1.384 ± 0.355	439.18	581.96	16572	0.364

Dilation	DSC	HD95/mm	%\|J_ϕ\|≤0/%	Params/k	Flops/G	Memory/MB	Time/s (GPU)
1,2,1,2,2	0.753 ± 0.027	2.148 ± 0.820	1.541 ± 0.339	439.18	581.96	16572	0.363
1,1,2,2,2	0.749 ± 0.026	2.108 ± 0.737	1.393 ± 0.313	439.18	581.96	16572	0.364
1,1,1,2,3	0.750 ± 0.026	2.254 ± 0.789	1.398 ± 0.318	439.18	581.96	16572	0.364
1,1,1,2,2	0.751 ± 0.027	2.107 ± 0.765	1.384 ± 0.355	439.18	581.96	16572	0.364