LIBR+: Improving Intraoperative Liver Registration by Learning the Residual of Biomechanics-Based Deformable Registration

TL;DR

This paper introduces LIBR+, a hybrid intraoperative liver registration method combining biomechanics and deep learning, which improves accuracy by learning residual deformations from sparse measurements.

Contribution

It proposes a novel dual-branch neural network that enhances biomechanical registration with learned residuals, addressing data sparsity and variability challenges.

Findings

LIBR+ outperforms existing methods in accuracy.

The dual-branch SR-GCN effectively propagates intraoperative measurements.

Experiments show consistent improvements over traditional and deep learning approaches.

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 \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.