LIBR+: Improving Intraoperative Liver Registration by Learning the
  Residual of Biomechanics-Based Deformable Registration
Dingrong Wang Soheil Azadvar Jon Heiselman Xiajun Jiang Michael Miga Linwei Wang
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.