Che Tongtong, Wang Xiuying, Zhao Kun, Zhao Yan, Zeng Debin, Li Qiongling, Zheng Yuanjie, Yang Ning, Wang Jian, Li Shuyu
School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
School of Computer Science, The University of Sydney, Sydney, Australia.
Med Image Anal. 2023 Apr;85:102740. doi: 10.1016/j.media.2023.102740. Epub 2023 Jan 13.
Three-dimensional (3D) deformable image registration is a fundamental technique in medical image analysis tasks. Although it has been extensively investigated, current deep-learning-based registration models may face the challenges posed by deformations with various degrees of complexity. This paper proposes an adaptive multi-level registration network (AMNet) to retain the continuity of the deformation field and to achieve high-performance registration for 3D brain MR images. First, we design a lightweight registration network with an adaptive growth strategy to learn deformation field from multi-level wavelet sub-bands, which facilitates both global and local optimization and achieves registration with high performance. Second, our AMNet is designed for image-wise registration, which adapts the local importance of a region in accordance with the complexity degrees of its deformation, and thereafter improves the registration efficiency and maintains the continuity of the deformation field. Experimental results from five publicly-available brain MR datasets and a synthetic brain MR dataset show that our method achieves superior performance against state-of-the-art medical image registration approaches.
三维（3D）可变形图像配准是医学图像分析任务中的一项基础技术。尽管已经对其进行了广泛研究，但当前基于深度学习的配准模型可能面临各种复杂程度变形带来的挑战。本文提出了一种自适应多级配准网络（AMNet），以保持变形场的连续性，并实现对3D脑磁共振图像的高性能配准。首先，我们设计了一个具有自适应增长策略的轻量级配准网络，从多级小波子带中学习变形场，这有助于全局和局部优化，并实现高性能配准。其次，我们的AMNet专为图像级配准而设计，它根据区域变形的复杂程度调整区域的局部重要性，从而提高配准效率并保持变形场的连续性。来自五个公开可用的脑磁共振数据集和一个合成脑磁共振数据集的实验结果表明，我们的方法相对于现有最先进的医学图像配准方法具有卓越的性能。
