The surgical environment imposes unique challenges to the intraoperative registration of organ shapes to their preoperatively-imaged geometry. Biomechanical model-based registration remains popular, while deep learning solutions remain limited due to the sparsity and variability of intraoperative measurements and the limited ground-truth deformation of an organ that can be obtained during the surgery. In this paper, we propose a novel \textit{hybrid} registration approach that leverage a linearized iterative boundary reconstruction (LIBR) method based on linear elastic biomechanics, and use deep neural networks to learn its residual to the ground-truth deformation (LIBR+). We further formulate a dual-branch spline-residual graph convolutional neural network (SR-GCN) to assimilate information from sparse and variable intraoperative measurements and effectively propagate it through the geometry of the 3D organ. Experiments on a large intraoperative liver registration dataset demonstrated the consistent improvements achieved by LIBR+ in comparison to existing rigid, biomechnical model-based non-rigid, and deep-learning based non-rigid approaches to intraoperative liver registration.

在这篇论文中，我们提出了一种新颖的混合注册方法，利用基于线性弹性生物力学的线性化迭代边界重建（LIBR）方法，并使用深度神经网络学习其与真实变形之间的残差（LIBR+）。我们进一步提出了一种双分支样条残差图卷积神经网络（SR-GCN），以吸收稀疏和可变的术中测量信息，并通过3D器官的几何结构有效传播。在大型术中肝脏注册数据集上的实验表明，与现有的刚性、基于生物力学模型的非刚性和基于深度学习的非刚性方法相比，LIBR+在术中肝脏注册中取得了一致的改进。

LIBR+: 基于生物力学的可变形注册的残差学习优化术中肝脏注册