Adaptive Model-Based Visual Stabilization of Image Sequences Using Feedback

Visual stabilization proposed in this paper compensates changes of the scene caused by motion and deformation of an observed object. This is of high importance in computer-assisted beating heart surgery, where the views of the beating heart should be stabilized. The proposed model-based method defines visual stabilization as a transformation of the current image sequence to a stabilized image sequence. This transformation incorporates physical model of the observed object and model of the measurement process. In contrast to standard approaches, the quality of the visual stabilization is continuously evaluated and improved in two aspects. On the one hand, discretization errors are reduced. On the other hand, the parameters of the underlying models are adjusted. The performance of the proposed method is evaluated in an experiment with a pressure-regulated artificial heart. Compared with standard methods, the model-based method provides higher accuracy, which is additionally improved by a feedback mechanism

Simultaneous State and Parameter Estimation for Physics-Based Tracking of Heart Surface Motion

Most existing approaches for tracking of the beating heart motion assume known cardiac kinematics and material parameters. However, these assumptions are not realistic for application in beating heart surgery. In this paper, a novel probabilistic tracking approach based on a physical model of the heart surface is presented. In contrast to existing approaches, the physical information about heart kinematics and material properties is incorporated and considered in an estimation of the heart behavior. An additional advantage is that the time-dependencies and uncertainties of the heart parameters are efficiently handled by exploiting simultaneous state and parameter estimation. Furthermore, by decomposing the state into linear and nonlinear substructures, the computational complexity of the estimation problem is reduced. The experimental results demonstrate the high performance of the method proposed in this paper. The solution of the parameter identification problem allows a personalized physical model and opens up possibilities to apply the physics-based tracking of the heart surface motion in a clinical environment

Heart Surface Motion Estimation Framework for Robotic Surgery Employing Meshless Methods

A novel heart surface motion estimation frame- work for a robotic surgery on a stabilized beating heart is proposed. It includes an approach for the reconstruction and prediction of heart surface motion based on a novel physical model of the intervention area described by a distributed- parameter system. Instead of conventional element methods, a meshless method is used for a spatial and temporal decomposi- tion of this system. This leads to a finite-dimensional state-space form. Furthermore, the state of the resulting lumped-parameter system, which provides an approximation of the deflection and velocity of the heart surface, is dynamically estimated under consideration of uncertainties both occurring in the system and arising from noisy camera measurements. By using the estimation results, an accurate reconstruction of heart surface motion for the synchronisation of the surgical instruments is also achieved at occluded or non-measurement points

Efficient Physics-Based Tracking of Heart Surface Motion for Beating Heart Surgery Robotic Systems

Purpose: Tracking of beating heart motion in a robotic surgery system is required for complex cardiovascular interventions. Methods: A heart surface motion tracking method is developed, including a stochastic physics-based heart surface model and an efficient reconstruction algorithm. The algorithm uses the constraints provided by the model that exploits the physical characteristics of the heart. The main advantage of the model is that it is more realistic than most standard heartmodels. Additionally, no explicit matching between the measurements and the model is required. The application of meshless methods significantly reduces the complexity of physics-based tracking. Results: Based on the stochastic physical model of the heart surface, this approach considers the motion of the intervention area and is robust to occlusions and reflections. The tracking algorithm is evaluated in simulations and experiments on an artificial heart. Providing higher accuracy than the standardmodel-based methods, it successfully copes with occlusions and provides high performance even when all measurements are not available. Conclusions: Combining the physical and stochastic description of the heart surface motion ensures physically correct and accurate prediction. Automatic initialization of the physics-based cardiac motion tracking enables system evaluation in a clinical environment

Visual Stabilization of Beating Heart Motion by Model-based Transformation of Image Sequences

In order to assist a surgeon by operating on a beating heart, visual stabilization makes the beating heart appear still to a surgeon by providing the current heart view as stationary and non-moving. In this way, the surgeon is not disturbed during an operation by a motion of the heart and can get an impression of performing conventional surgery. In contrast to existing methods for visual stabilization, the proposed approach involves a model-based transformation of image sequences provided by a camera system. This transformation incorporates the knowledge of physical characteristics of the heart in form of a mathematical model of the heart surface. Its main advantage is that the uncertainties of the model and measurements are considered. This occurs by estimating the parameters of the transformation. Furthermore, the quality of the visual stabilization is additionally improved by adapting the parameters of the underlying physical model. A performance of the proposed approach is evaluated in an experiment with a pressure-regulated artificial heart. In comparison to standard approaches, it provides superior results illustrating the high quality of the visual stabilization

G (2009) Heart surface motion estimation framework for robotic surgery employing meshless methods

Abstract-A novel heart surface motion estimation framework for a robotic surgery on a stabilized beating heart is proposed. It includes an approach for the reconstruction and prediction of heart surface motion based on a novel physical model of the intervention area described by a distributedparameter system. Instead of conventional element methods, a meshless method is used for a spatial and temporal decomposition of this system. This leads to a finite-dimensional state-space form. Furthermore, the state of the resulting lumped-parameter system, which provides an approximation of the deflection and velocity of the heart surface, is dynamically estimated under consideration of uncertainties both occurring in the system and arising from noisy camera measurements. By using the estimation results, an accurate reconstruction of heart surface motion for the synchronisation of the surgical instruments is also achieved at occluded or non-measurement points

Hoisington Files, undated

https://deepblue.lib.umich.edu/bitstream/2027.42/142795/2/objects.ziphttps://deepblue.lib.umich.edu/bitstream/2027.42/142795/4/metadata.zi