bibtext: @InProceedings{ Wan_LIBR_MICCAI2024,
                   author = { Wang, Dingrong and Azadvar, Soheil and Heiselman, Jon and Jiang, Xiajun and Miga, Michael and Wang, Linwei },
                   title = { { LIBR+: Improving Intraoperative Liver Registration by Learning the Residual of Biomechanics-Based Deformable Registration } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15006 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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 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 dualbranch 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.
title: LIBR+: Improving Intraoperative Liver Registration by Learning the Residual of Biomechanics-Based Deformable Registration
authors: Wang, Dingrong Azadvar, Soheil Heiselman, Jon Jiang, Xiajun Miga, Michael Wang, Linwei