干旱区生态环境知识资源中心(Arid): HGCMorph: joint discontinuity-preserving and pose-learning via GNN and capsule networks for deformable medical images registration

Arid

DOI	10.1088/1361-6560/ad2a96
	HGCMorph: joint discontinuity-preserving and pose-learning via GNN and capsule networks for deformable medical images registration
	Yan, Zhiyue; Ji, Jianhua; Ma, Jia; Cao, Wenming
通讯作者	Cao, WM
来源期刊	PHYSICS IN MEDICINE AND BIOLOGY
ISSN	0031-9155
EISSN	1361-6560
出版年	2024
卷号	69 期号:7
英文摘要	Objective. This study aims to enhance medical image registration by addressing the limitations of existing approaches that rely on spatial transformations through U-Net, ConvNets, or Transformers. The objective is to develop a novel architecture that combines ConvNets, graph neural networks (GNNs), and capsule networks to improve the accuracy and efficiency of medical image registration, which can also deal with the problem of rotating registration. Approach. We propose an deep learning-based approach which can be utilized in both unsupervised and semi-supervised manners, named as HGCMorph. It leverages a hybrid framework that integrates ConvNets and GNNs to capture lower-level features, specifically short-range attention, while also utilizing capsule networks (CapsNets) to model abstract higher-level features, including entity properties such as position, size, orientation, deformation, and texture. This hybrid framework aims to provide a comprehensive representation of anatomical structures and their spatial relationships in medical images. Main results. The results demonstrate the superiority of HGCMorph over existing state-of-the-art deep learning-based methods in both qualitative and quantitative evaluations. In unsupervised training process, our model outperforms the recent SOTA method TransMorph by achieving 7%/38% increase on Dice score coefficient (DSC), and 2%/7% improvement on negative jacobian determinant for OASIS and LPBA40 datasets, respectively. Furthermore, HGCMorph achieves improved registration accuracy in semi-supervised training process. In addition, when dealing with complex 3D rotations and secondary randomly deformations, our method still achieves the best performance. We also tested our methods on lung datasets, such as Japanese Society of Radiology, Montgoermy and Shenzhen. Significance. The significance lies in its innovative design to medical image registration. HGCMorph offers a novel framework that overcomes the limitations of existing methods by efficiently capturing both local and abstract features, leading to enhanced registration accuracy, discontinuity-preserving, and pose-learning abilities. The incorporation of capsule networks introduces valuable improvements, making the proposed method a valuable contribution to the field of medical image analysis. HGCMorph not only advances the SOTA methods but also has the potential to improve various medical applications that rely on accurate image registration.
英文关键词	magnetic resonance imaging deformable image registration deep learning unsupervised weakly supervised neural network
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001193275000001
WOS关键词	FREE-FORM DEFORMATION ; NONRIGID REGISTRATION
WOS类目	Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS研究方向	Engineering ; Radiology, Nuclear Medicine & Medical Imaging
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405063
推荐引用方式 GB/T 7714	Yan, Zhiyue,Ji, Jianhua,Ma, Jia,et al. HGCMorph: joint discontinuity-preserving and pose-learning via GNN and capsule networks for deformable medical images registration[J],2024,69(7).
APA	Yan, Zhiyue,Ji, Jianhua,Ma, Jia,&Cao, Wenming.(2024).HGCMorph: joint discontinuity-preserving and pose-learning via GNN and capsule networks for deformable medical images registration.PHYSICS IN MEDICINE AND BIOLOGY,69(7).
MLA	Yan, Zhiyue,et al."HGCMorph: joint discontinuity-preserving and pose-learning via GNN and capsule networks for deformable medical images registration".PHYSICS IN MEDICINE AND BIOLOGY 69.7(2024).