CMAN: Cascaded Multi-scale Spatial Channel Attention-guided Network for large 3D deformable registration of liver CT images

• Published in: Medical image analysis 2024-08, Vol.96, p.103212, Article 103212
• Main Authors: Pham, Xuan Loc, Luu, Manh Ha, van Walsum, Theo, Mai, Hong Son, Klein, Stefan, Le, Ngoc Ha, Chu, Duc Trinh
• Format: Article
• Language: English
• Subjects: 3D medical image registration; Cascaded network; Double coarse-to-fine registration; Liver computed tomography; Spatial-channel attention
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2024.103212

Deformable image registration is an essential component of medical image analysis and plays an irreplaceable role in clinical practice. In recent years, deep learning-based registration methods have demonstrated significant improvements in convenience, robustness and execution time compared to traditional algorithms. However, registering images with large displacements, such as those of the liver organ, remains underexplored and challenging. In this study, we present a novel convolutional neural network (CNN)-based unsupervised learning registration method, Cascaded Multi-scale Spatial-Channel Attention-guided Network (CMAN), which addresses the challenge of large deformation fields using a double coarse-to-fine registration approach. The main contributions of CMAN include: (i) local coarse-to-fine registration in the base network, which generates the displacement field for each resolution and progressively propagates these local deformations as auxiliary information for the final deformation field; (ii) global coarse-to-fine registration, which stacks multiple base networks for sequential warping, thereby incorporating richer multi-layer contextual details into the final deformation field; (iii) integration of the spatial-channel attention module in the decoder stage, which better highlights important features and improves the quality of feature maps. The proposed network was trained using two public datasets and evaluated on another public dataset as well as a private dataset across several experimental scenarios. We compared CMAN with four state-of-the-art CNN-based registration methods and two well-known traditional algorithms. The results show that the proposed double coarse-to-fine registration strategy outperforms other methods in most registration evaluation metrics. In conclusion, CMAN can effectively handle the large-deformation registration problem and show potential for application in clinical practice. The source code is made publicly available at https://github.com/LocPham263/CMAN.git. •CMAN is designed to address the large deformation field between liver CT images using a double coarse-to-fine registration approach.•The spatial-channel attention module in the decoder stage highlights key features and enhances feature map quality at every resolution.•The study used both public and private liver datasets divided into four types of CT images, each tagged with a specific deformation level.•The proposed method was thoroughly assessed against well