Deep-learning-based deformable image registration (DL-DIR) has demonstrated improved accuracy compared to time-consuming non-DL methods across various anatomical sites. However, DL-DIR is still challenging in heterogeneous tissue regions with large deformation. In fact, several state-of-the-art DL-DIR methods fail to capture the large, anatomically plausible deformation when tested on head-and-neck computed tomography (CT) images. These results allude to the possibility that such complex head-and-neck deformation may be beyond the capacity of a single network structure or a homogeneous smoothness regularization. To address the challenge of combined multi-scale musculoskeletal motion and soft tissue deformation in the head-and-neck region, we propose a MUsculo-Skeleton-Aware (MUSA) framework to anatomically guide DL-DIR by leveraging the explicit multiresolution strategy and the inhomogeneous deformation constraints between the bony structures and soft tissue. The proposed method decomposes the complex deformation into a bulk posture change and residual fine deformation. It can accommodate both inter- and intra- subject registration. Our results show that the MUSA framework can consistently improve registration accuracy and, more importantly, the plausibility of deformation for various network architectures. The code will be publicly available at https://github.com/HengjieLiu/DIR-MUSA.

中文翻译：

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MUsculo-Skeleton-Aware （MUSA） 深度学习，用于解剖引导的头颈部 CT 可变形配准


与耗时的非 DL 方法相比，基于深度学习的可变形图像配准 （DL-DIR） 在各种解剖部位的准确性更高。然而，DL-DIR 在具有大变形的异质组织区域中仍然具有挑战性。事实上，在头颈部计算机断层扫描 （CT） 图像上进行测试时，几种最先进的 DL-DIR 方法无法捕捉到大的、解剖学上合理的变形。这些结果表明，这种复杂的头颈变形可能超出单个网络结构或均匀平滑正则化的能力。为了解决头颈部区域多尺度肌肉骨骼运动和软组织变形相结合的挑战，我们提出了一个肌肉骨骼感知 （MUSA） 框架，通过利用显式多分辨率策略和骨骼结构和软组织之间的不均匀变形约束，在解剖学上指导 DL-DIR。所提方法将复杂变形分解为体态态变化和残余细小变形。它可以容纳主题间和主题内注册。我们的结果表明，MUSA 框架可以持续提高配准精度，更重要的是，可以提高各种网络架构的变形合理性。该代码将在 https://github.com/HengjieLiu/DIR-MUSA 上公开提供。