# A Review on Organ Deformation Modeling Approaches for Reliable Surgical Navigation using Augmented Reality arXiv: https://arxiv.org/abs/2408.02713 ยท HTML: https://mlsanity.com/paper/2408.02713/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 31 of its 193 references that are in the map. - First posted on arXiv 5 Aug 2024. ## Builds on (most cited first, 30 of 31) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015) - [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016) - [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016) - [Human Pose Estimation with Iterative Error Feedback](https://mlsanity.com/paper/1507.06550/) (2015) - [Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registration](https://mlsanity.com/paper/2206.08903/) (2022) - [Physics-Guided Deep Learning for Dynamical Systems: A Survey](https://mlsanity.com/paper/2107.01272/) (2021) - [Physics guided machine learning using simplified theories](https://mlsanity.com/paper/2012.13343/) (2020) - [PhysGNN: A Physics-Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image-Guided Neurosurgery](https://mlsanity.com/paper/2109.04352/) (2021) - [Learning Soft Tissue Behavior of Organs for Surgical Navigation with Convolutional Neural Networks](https://mlsanity.com/paper/1904.00722/) (2019) - [Non-Rigid Volume to Surface Registration using a Data-Driven Biomechanical Model](https://mlsanity.com/paper/2005.14695/) (2020) - [An objective comparison of methods for augmented reality in laparoscopic liver resection by preoperative-to-intraoperative image fusion](https://mlsanity.com/paper/2401.15753/) (2024) - [Learning Occupancy Function from Point Clouds for Surface Reconstruction](https://mlsanity.com/paper/2010.11378/) (2020) - [Boundary Constraint-free Biomechanical Model-Based Surface Matching for Intraoperative Liver Deformation Correction](https://mlsanity.com/paper/2403.09964/) (2024) - [Real-to-Sim Registration of Deformable Soft Tissue with Position-Based Dynamics for Surgical Robot Autonomy](https://mlsanity.com/paper/2011.00800/) (2020) - [Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery](https://mlsanity.com/paper/2308.02917/) (2023) - [Utility of Optical See-Through Head Mounted Displays in Augmented Reality-Assisted Surgery: A systematic review](https://mlsanity.com/paper/2202.04141/) (2022) - [Non-rigid Medical Image Registration using Physics-informed Neural Networks](https://mlsanity.com/paper/2302.10343/) (2023) - [IGCN: Image-to-graph Convolutional Network for 2D/3D Deformable Registration](https://mlsanity.com/paper/2111.00484/) (2021) - [The HoloLens in Medicine: A systematic Review and Taxonomy](https://mlsanity.com/paper/2209.03245/) (2022) - [Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning](https://mlsanity.com/paper/2301.04614/) (2023) - [Prostate motion modelling using biomechanically-trained deep neural networks on unstructured nodes](https://mlsanity.com/paper/2007.04972/) (2020) - [An Optimal Control Problem for Elastic Registration and Force Estimation in Augmented Surgery](https://mlsanity.com/paper/2206.10931/) (2022) - [Comparing Regularized Kelvinlet Functions and the Finite Element Method for Registration of Medical Images to Sparse Organ Data](https://mlsanity.com/paper/2306.05509/) (2023) - [Iterative PnP and its application in 3D-2D vascular image registration for robot navigation](https://mlsanity.com/paper/2310.12551/) (2023) - [LIBR+: Improving Intraoperative Liver Registration by Learning the Residual of Biomechanics-Based Deformable Registration](https://mlsanity.com/paper/2403.06901/) (2024) - [A Real-time and Registration-free Framework for Dynamic Shape Instantiation](https://mlsanity.com/paper/1801.00182/) (2017) - [Elastic registration based on compliance analysis and biomechanical graph matching](https://mlsanity.com/paper/1912.06353/) (2019) - [Kernel-based framework to estimate deformations of pneumothorax lung using relative position of anatomical landmarks](https://mlsanity.com/paper/2102.12505/) (2021) - [Real-time elastic partial shape matching using a neural network-based adjoint method](https://mlsanity.com/paper/2303.09343/) (2023) ## Built on by (most cited first, 5 of 5) - [Toward Reliable AR-Guided Surgical Navigation: Interactive Deformation Modeling with Data-Driven Biomechanics and Prompts](https://mlsanity.com/paper/2506.08048/) (2025) - [DINE: Distance Is Not Enough -- Learning Global Deformation Priors for Robust Soft-Tissue Point Cloud Registration](https://mlsanity.com/paper/2607.14946/) (2026) - [ARport: An Augmented Reality System for Markerless Image-Guided Port Placement in Robotic Surgery](https://mlsanity.com/paper/2602.14153/) (2026) - [SurgFormer: Scalable Learning of Organ Deformation with Resection Support and Real-Time Inference](https://mlsanity.com/paper/2603.06543/) (2026) - [Evaluating Hybrid Quantum-Classical Models for Reduced-Order Brain Deformation Dynamics](https://mlsanity.com/paper/2610.00554/) (2026) ## Abstract (arXiv) Augmented Reality (AR) holds the potential to revolutionize surgical procedures by allowing surgeons to visualize critical structures within the patient's body. This is achieved through superimposing preoperative organ models onto the actual anatomy. Challenges arise from dynamic deformations of organs during surgery, making preoperative models inadequate for faithfully representing intraoperative anatomy. To enable reliable navigation in augmented surgery, modeling of intraoperative deformation to obtain an accurate alignment of the preoperative organ model with the intraoperative anatomy is indispensable. Despite the existence of various methods proposed to model intraoperative organ deformation, there are still few literature reviews that systematically categorize and summarize these approaches. This review aims to fill this gap by providing a comprehensive and technical-oriented overview of modeling methods for intraoperative organ deformation in augmented reality in surgery. Through a systematic search and screening process, 112 closely relevant papers were included in this review. By presenting the current status of organ deformation modeling methods and their clinical applications, this review seeks to enhance the understanding of organ deformation modeling in AR-guided surgery, and discuss the potential topics for future advancements.