Simulation of hyperelastic materials in real-time using Deep Learning

The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition, parallel computing, adaptive meshing, and model order reduction. In this paper we present U-Mesh: a data-driven method based on a U-Net architecture that approximates the non-linear relation between a contact force and the displacement field computed by a FEM algorithm. We show that deep learning, one of the latest machine learning methods based on artificial neural networks, can enhance computational mechanics through its ability to encode highly non-linear models in a compact form. Our method is applied to two benchmark examples: a cantilever beam and an L-shape subject to moving punctual loads. A comparison between our method and proper orthogonal decomposition (POD) is done through the paper. The results show that U-Mesh can perform very fast simulations on various geometries, mesh resolutions and number of input forces with very small errors

Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence

Mobile Augmented Reality (MAR) integrates computer-generated virtual objects with physical environments for mobile devices. MAR systems enable users to interact with MAR devices, such as smartphones and head-worn wearables, and perform seamless transitions from the physical world to a mixed world with digital entities. These MAR systems support user experiences using MAR devices to provide universal access to digital content. Over the past 20 years, several MAR systems have been developed, however, the studies and design of MAR frameworks have not yet been systematically reviewed from the perspective of user-centric design. This article presents the first effort of surveying existing MAR frameworks (count: 37) and further discuss the latest studies on MAR through a top-down approach: (1) MAR applications; (2) MAR visualisation techniques adaptive to user mobility and contexts; (3) systematic evaluation of MAR frameworks, including supported platforms and corresponding features such as tracking, feature extraction, and sensing capabilities; and (4) underlying machine learning approaches supporting intelligent operations within MAR systems. Finally, we summarise the development of emerging research fields and the current state-of-the-art, and discuss the important open challenges and possible theoretical and technical directions. This survey aims to benefit both researchers and MAR system developers alike.Peer reviewe

Non-Rigid Liver Registration for Laparoscopy using Data-Driven Biomechanical Models

During laparoscopic liver resection, the limited access to the organ, the small field of view and lack of palpation can obstruct a surgeon’s workflow. Automatic navigation systems could use the images from preoperative volumetric organ scans to help the surgeons find their target (tumors) and risk-structures (vessels) more efficiently. This requires the preoperative data to be fused (or registered) with the intraoperative scene in order to display information at the correct intraoperative position. One key challenge in this setting is the automatic estimation of the organ’s current intra-operative deformation, which is required in order to predict the position of internal structures. Parameterizing the many patient-specific unknowns (tissue properties, boundary conditions, interactions with other tissues, direction of gravity) is very difficult. Instead, this work explores how to employ deep neural networks to solve the registration problem in a data-driven manner. To this end, convolutional neural networks are trained on synthetic data to estimate an organ’s intraoperative displacement field and thus its current deformation. To drive this estimation, visible surface cues from the intraoperative camera view must be supplied to the networks. Since reliable surface features are very difficult to find, the networks are adapted to also find correspondences between the pre- and intraoperative liver geometry automatically. This combines the search for correspondences with the biomechanical behavior estimation and allows the networks to tackle the full non-rigid registration problem in one single step. The result is a model which can quickly predict the volume deformation of a liver, given only sparse surface information. The model combines the advantages of a physically accurate biomechanical simulation with the speed and powerful feature extraction capabilities of deep neural networks. To test the method intraoperatively, a registration pipeline is developed which constructs a map of the liver and its surroundings from the laparoscopic video and then uses the neural networks to fuse the preoperative volume data into this map. The deformed organ volume can then be rendered as an overlay directly onto the laparoscopic video stream. The focus of this pipeline is to be applicable to real surgery, where everything should be quick and non-intrusive. To meet these requirements, a SLAM system is used to localize the laparoscopic camera (avoiding setup of an external tracking system), various neural networks are used to quickly interpret the scene and semi-automatic tools let the surgeons guide the system. Beyond the concrete advantages of the data-driven approach for intraoperative registration, this work also demonstrates general benefits of training a registration system preoperatively on synthetic data. The method lets the engineer decide which values need to be known explicitly and which should be estimated implicitly by the networks, which opens the door to many new possibilities.:1 Introduction 1.1 Motivation 1.1.1 Navigated Liver Surgery 1.1.2 Laparoscopic Liver Registration 1.2 Challenges in Laparoscopic Liver Registration 1.2.1 Preoperative Model 1.2.2 Intraoperative Data 1.2.3 Fusion/Registration 1.2.4 Data 1.3 Scope and Goals of this Work 1.3.1 Data-Driven, Biomechanical Model 1.3.2 Data-Driven Non-Rigid Registration 1.3.3 Building a Working Prototype 2 State of the Art 2.1 Rigid Registration 2.2 Non-Rigid Liver Registration 2.3 Neural Networks for Simulation and Registration 3 Theoretical Background 3.1 Liver 3.2 Laparoscopic Liver Resection 3.2.1 Staging Procedure 3.3 Biomechanical Simulation 3.3.1 Physical Balance Principles 3.3.2 Material Models 3.3.3 Numerical Solver: The Finite Element Method (FEM) 3.3.4 The Lagrangian Specification 3.4 Variables and Data in Liver Registration 3.4.1 Observable 3.4.2 Unknowns 4 Generating Simulations of Deforming Organs 4.1 Organ Volume 4.2 Forces and Boundary Conditions 4.2.1 Surface Forces 4.2.2 Zero-Displacement Boundary Conditions 4.2.3 Surrounding Tissues and Ligaments 4.2.4 Gravity 4.2.5 Pressure 4.3 Simulation 4.3.1 Static Simulation 4.3.2 Dynamic Simulation 4.4 Surface Extraction 4.4.1 Partial Surface Extraction 4.4.2 Surface Noise 4.4.3 Partial Surface Displacement 4.5 Voxelization 4.5.1 Voxelizing the Liver Geometry 4.5.2 Voxelizing the Displacement Field 4.5.3 Voxelizing Boundary Conditions 4.6 Pruning Dataset - Removing Unwanted Results 4.7 Data Augmentation 5 Deep Neural Networks for Biomechanical Simulation 5.1 Training Data 5.2 Network Architecture 5.3 Loss Functions and Training 6 Deep Neural Networks for Non-Rigid Registration 6.1 Training Data 6.2 Architecture 6.3 Loss 6.4 Training 6.5 Mesh Deformation 6.6 Example Application 7 Intraoperative Prototype 7.1 Image Acquisition 7.2 Stereo Calibration 7.3 Image Rectification, Disparity- and Depth- estimation 7.4 Liver Segmentation 7.4.1 Synthetic Image Generation 7.4.2 Automatic Segmentation 7.4.3 Manual Segmentation Modifier 7.5 SLAM 7.6 Dense Reconstruction 7.7 Rigid Registration 7.8 Non-Rigid Registration 7.9 Rendering 7.10 Robotic Operating System 8 Evaluation 8.1 Evaluation Datasets 8.1.1 In-Silico 8.1.2 Phantom Torso and Liver 8.1.3 In-Vivo, Human, Breathing Motion 8.1.4 In-Vivo, Human, Laparoscopy 8.2 Metrics 8.2.1 Mean Displacement Error 8.2.2 Target Registration Error (TRE) 8.2.3 Champfer Distance 8.2.4 Volumetric Change 8.3 Evaluation of the Synthetic Training Data 8.4 Data-Driven Biomechanical Model (DDBM) 8.4.1 Amount of Intraoperative Surface 8.4.2 Dynamic Simulation 8.5 Volume to Surface Registration Network (V2S-Net) 8.5.1 Amount of Intraoperative Surface 8.5.2 Dependency on Initial Rigid Alignment 8.5.3 Registration Accuracy in Comparison to Surface Noise 8.5.4 Registration Accuracy in Comparison to Material Stiffness 8.5.5 Champfer-Distance vs. Mean Displacement Error 8.5.6 In-vivo, Human Breathing Motion 8.6 Full Intraoperative Pipeline 8.6.1 Intraoperative Reconstruction: SLAM and Intraoperative Map 8.6.2 Full Pipeline on Laparoscopic Human Data 8.7 Timing 9 Discussion 9.1 Intraoperative Model 9.2 Physical Accuracy 9.3 Limitations in Training Data 9.4 Limitations Caused by Difference in Pre- and Intraoperative Modalities 9.5 Ambiguity 9.6 Intraoperative Prototype 10 Conclusion 11 List of Publications List of Figures Bibliograph

Intraoperative Liver Surface Completion with Graph Convolutional VAE

In this work we propose a method based on geometric deep learning to predict the complete surface of the liver, given a partial point cloud of the organ obtained during the surgical laparoscopic procedure. We introduce a new data augmentation technique that randomly perturbs shapes in their frequency domain to compensate the limited size of our dataset. The core of our method is a variational autoencoder (VAE) that is trained to learn a latent space for complete shapes of the liver. At inference time, the generative part of the model is embedded in an optimisation procedure where the latent representation is iteratively updated to generate a model that matches the intraoperative partial point cloud. The effect of this optimisation is a progressive non-rigid deformation of the initially generated shape. Our method is qualitatively evaluated on real data and quantitatively evaluated on synthetic data. We compared with a state-of-the-art rigid registration algorithm, that our method outperformed in visible areas

Automatic, global registration in laparoscopic liver surgery

PURPOSE: The initial registration of a 3D pre-operative CT model to a 2D laparoscopic video image in augmented reality systems for liver surgery needs to be fast, intuitive to perform and with minimal interruptions to the surgical intervention. Several recent methods have focussed on using easily recognisable landmarks across modalities. However, these methods still need manual annotation or manual alignment. We propose a novel, fully automatic pipeline for 3D-2D global registration in laparoscopic liver interventions. METHODS: Firstly, we train a fully convolutional network for the semantic detection of liver contours in laparoscopic images. Secondly, we propose a novel contour-based global registration algorithm to estimate the camera pose without any manual input during surgery. The contours used are the anterior ridge and the silhouette of the liver. RESULTS: We show excellent generalisation of the semantic contour detection on test data from 8 clinical cases. In quantitative experiments, the proposed contour-based registration can successfully estimate a global alignment with as little as 30% of the liver surface, a visibility ratio which is characteristic of laparoscopic interventions. Moreover, the proposed pipeline showed very promising results in clinical data from 5 laparoscopic interventions. CONCLUSIONS: Our proposed automatic global registration could make augmented reality systems more intuitive and usable for surgeons and easier to translate to operating rooms. Yet, as the liver is deformed significantly during surgery, it will be very beneficial to incorporate deformation into our method for more accurate registration

Performance of image guided navigation in laparoscopic liver surgery – A systematic review

Background: Compared to open surgery, minimally invasive liver resection has improved short term outcomes. It is however technically more challenging. Navigated image guidance systems (IGS) are being developed to overcome these challenges. The aim of this systematic review is to provide an overview of their current capabilities and limitations. Methods: Medline, Embase and Cochrane databases were searched using free text terms and corresponding controlled vocabulary. Titles and abstracts of retrieved articles were screened for inclusion criteria. Due to the heterogeneity of the retrieved data it was not possible to conduct a meta-analysis. Therefore results are presented in tabulated and narrative format. Results: Out of 2015 articles, 17 pre-clinical and 33 clinical papers met inclusion criteria. Data from 24 articles that reported on accuracy indicates that in recent years navigation accuracy has been in the range of 8–15 mm. Due to discrepancies in evaluation methods it is difficult to compare accuracy metrics between different systems. Surgeon feedback suggests that current state of the art IGS may be useful as a supplementary navigation tool, especially in small liver lesions that are difficult to locate. They are however not able to reliably localise all relevant anatomical structures. Only one article investigated IGS impact on clinical outcomes. Conclusions: Further improvements in navigation accuracy are needed to enable reliable visualisation of tumour margins with the precision required for oncological resections. To enhance comparability between different IGS it is crucial to find a consensus on the assessment of navigation accuracy as a minimum reporting standard