Medical image registration plays an important role in medical diagnosis and treatment planning. However, the current registration methods based on deep learning still face some challenges, such as insufficient ability to extract global information, large number of network model parameters, slow reasoning speed and so on. Therefore, this paper proposed a new model LCU-Net, which used parallel lightweight convolution to improve the ability of global information extraction. The problem of large number of network parameters and slow inference speed was solved by multi-scale fusion. The experimental results showed that the Dice coefficient of LCU-Net reached 0.823, the Hausdorff distance was 1.258, and the number of network parameters was reduced by about one quarter compared with that before multi-scale fusion. The proposed algorithm shows remarkable advantages in medical image registration tasks, and it not only surpasses the existing comparison algorithms in performance, but also has excellent generalization performance and wide application prospects.
医学图像配准在医疗诊断和治疗规划等领域具有重要意义。然而，当前基于深度学习的配准方法仍然面临着一些挑战，如对全局信息提取能力不足、网络模型参数量大、推理速度慢等问题。为此，本文提出了一种新的模型LCU-Net，采用并行轻量化卷积以提升全局信息的提取能力；通过多尺度融合来解决网络参数量大和推理速度慢的问题。实验结果显示，LCU-Net的Dice系数达到0.823，Hausdorff距离为1.258，网络参数量相对于多尺度融合之前减少了约四分之一。本文提出的算法在医学图像配准任务中表现出显著优势，不仅在性能上超越了现有的对比算法，而且具有出色的泛化性能以及广泛的应用前景。.
Keywords: Lightweight model; Medical image processing; Multi-scale fusion; Parallel path.