School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China.
Med Biol Eng Comput. 2023 Sep;61(9):2353-2365. doi: 10.1007/s11517-023-02834-x. Epub 2023 Apr 18.
Lung image registration is more challenging than other organs. This is because the breath of the human body causes large deformations in the lung parenchyma and small deformations in tissues such as the pulmonary vascular. Many studies have recently used multi-resolution networks to solve the lung registration problem. However, they use the same structure of registration modules on each level, which makes it difficult to handle complex and small deformations. We propose an unsupervised heterogeneous multi-resolution network (UHMR-Net) to overcome the above problem. The image detail registration module (IDRM) is designed on the highest resolution level. Within this module, the cascaded network is used on the same resolution image to continuously learn the "remaining" detail deformation fields. The shallow shrinkage loss (SS-Loss) is designed to supervise the cascaded network, thus further improving the ability of the network to handle small deformations. Moreover, with the lightweight feature local correlation layer we proposed, the image boundary registration module (IBRM), on multiple low-resolution levels, can better solve the large deformation registration problem. The target registration error on the public DIR-Lab 4DCT dataset was 1.56 ± 1.39 mm, which was significantly better than the classic conventional methods and advanced deep-based methods.
肺图像配准比其他器官更具挑战性。这是因为人体的呼吸会导致肺实质产生较大的变形，而肺部血管等组织的变形则较小。最近有许多研究使用多分辨率网络来解决肺配准问题。然而，它们在每个级别上都使用相同的配准模块结构，这使得处理复杂和小变形变得困难。我们提出了一种无监督的异质多分辨率网络（UHMR-Net）来克服上述问题。图像细节配准模块（IDRM）设计在最高分辨率级别上。在这个模块中，级联网络用于同一分辨率的图像，以不断学习“剩余”细节变形场。设计浅层收缩损失（SS-Loss）来监督级联网络，从而进一步提高网络处理小变形的能力。此外，我们提出的轻量级特征局部相关层，用于多个低分辨率级别上的图像边界配准模块（IBRM），可以更好地解决大变形配准问题。在公共 DIR-Lab 4DCT 数据集上的目标配准误差为 1.56 ± 1.39 毫米，明显优于经典的传统方法和先进的基于深度学习的方法。
