Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (12): 3915-3921.DOI: 10.11772/j.issn.1001-9081.2023121828
• Multimedia computing and computer simulation • Previous Articles Next Articles
Xin ZHAO(), Xinjie LI, Jian XU, Buyun LIU, Xiang BI
Received:
2024-01-02
Revised:
2024-04-02
Accepted:
2024-04-07
Online:
2024-04-19
Published:
2024-12-10
Contact:
Xin ZHAO
About author:
LI Xinjie, born in 1999, M. S. candidate. His research interests include deep learning, medical image registration, computer vision.Supported by:
通讯作者:
赵欣
作者简介:
李鑫杰(1999—),男,河南驻马店人,硕士研究生,主要研究方向:深度学习、医学图像配准、计算机视觉基金资助:
CLC Number:
Xin ZHAO, Xinjie LI, Jian XU, Buyun LIU, Xiang BI. Parallel medical image registration model based on convolutional neural network and Transformer[J]. Journal of Computer Applications, 2024, 44(12): 3915-3921.
赵欣, 李鑫杰, 徐健, 刘步云, 毕祥. 基于卷积神经网络与Transformer并行的医学图像配准模型[J]. 《计算机应用》唯一官方网站, 2024, 44(12): 3915-3921.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023121828
模型名称 | Dice | 形变场折叠率/% | Params/106 | FLOPs/109 |
---|---|---|---|---|
SyN | 0.645 | <0.000 1 | — | — |
NiftyReg | 0.645 | 0.020 0 | — | — |
LDDMM | 0.680 | <0.000 1 | — | — |
VoxelMorph | 0.719 | 1.590 0 | 0.30 | 399.0 |
CycleMorph | 0.705 | 1.719 0 | 361.00 | — |
MIDIR | 0.732 | <0.000 1 | 0.27 | 47.0 |
ViT-V-Net | 0.727 | 1.609 0 | 31.50 | 389.2 |
Cotr | 0.728 | 1.292 0 | 38.70 | 2 447.6 |
nnFormer | 0.737 | 1.595 0 | 34.20 | 824.0 |
TransMorph | 0.739 | 1.579 0 | 46.70 | 713.5 |
PPSCNet | 0.742 | 0.137 0 | 58.00 | 987.9 |
PPCTNet | 0.744 | 0.012 5 | 36.31 | 435.5 |
Tab. 1 Model comparison results
模型名称 | Dice | 形变场折叠率/% | Params/106 | FLOPs/109 |
---|---|---|---|---|
SyN | 0.645 | <0.000 1 | — | — |
NiftyReg | 0.645 | 0.020 0 | — | — |
LDDMM | 0.680 | <0.000 1 | — | — |
VoxelMorph | 0.719 | 1.590 0 | 0.30 | 399.0 |
CycleMorph | 0.705 | 1.719 0 | 361.00 | — |
MIDIR | 0.732 | <0.000 1 | 0.27 | 47.0 |
ViT-V-Net | 0.727 | 1.609 0 | 31.50 | 389.2 |
Cotr | 0.728 | 1.292 0 | 38.70 | 2 447.6 |
nnFormer | 0.737 | 1.595 0 | 34.20 | 824.0 |
TransMorph | 0.739 | 1.579 0 | 46.70 | 713.5 |
PPSCNet | 0.742 | 0.137 0 | 58.00 | 987.9 |
PPCTNet | 0.744 | 0.012 5 | 36.31 | 435.5 |
模型 | Dice |
---|---|
Swin Transformer | 0.739 |
LACB(CNN) | 0.714 |
Swin+LACB+Bridging a | 0.728 |
Swin+LACB+Bridging b | 0.744 |
Tab. 2 Ablation study results
模型 | Dice |
---|---|
Swin Transformer | 0.739 |
LACB(CNN) | 0.714 |
Swin+LACB+Bridging a | 0.728 |
Swin+LACB+Bridging b | 0.744 |
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