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, (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 .

中文翻译：

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CMAN：级联多尺度空间通道注意力引导网络，用于肝脏 CT 图像的大型 3D 变形配准


变形图像配准是医学图像分析的重要组成部分，在临床实践中发挥着不可替代的作用。近年来，与传统算法相比，基于深度学习的配准方法在便利性、鲁棒性和执行时间方面表现出显着的改进。然而，配准具有大位移的图像（例如肝脏器官的图像）仍然尚未得到充分探索且具有挑战性。在这项研究中，我们提出了一种新颖的基于卷积神经网络（CNN）的无监督学习配准方法（CMAN），该方法使用从粗到精的双配准方法解决了大变形场的挑战。 CMAN的主要贡献包括：（i）基础网络中的局部粗到精配准，生成每个分辨率的位移场，并逐步传播这些局部变形作为最终变形场的辅助信息； (ii) 全局从粗到精的配准，堆叠多个基础网络进行顺序变形，从而将更丰富的多层上下文细节合并到最终的变形场中； （iii）在解码器阶段集成空间通道注意模块，更好地突出重要特征并提高特征图的质量。所提出的网络使用两个公共数据集进行训练，并在另一个公共数据集以及跨多个实验场景的私有数据集上进行评估。我们将 CMAN 与四种最先进的基于 CNN 的配准方法和两种著名的传统算法进行了比较。结果表明，所提出的双粗到精配准策略在大多数配准评估指标上都优于其他方法。 总之，CMAN可以有效处理大变形配准问题，并显示出在临床实践中的应用潜力。源代码在 上公开提供。