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ABSTRACT: One challenge in laparoscopic cholecystectomy surgery simulation is to construct a fast and accurate deformable gallbladder model. This paper proposed an improved multi-layer mass-spring modeling method which can adapt well to the built-in accelerating algorithms in PhysX-engine of GPU. A multi-layer mass-spring model was constructed based on the surface mesh of a gallbladder. The inner layers of the mass-spring model were generated using more geometrical information than the previous method. The parameters of the springs were configured based on the biomechanical properties of gallbladder to ensure the realism of the deformation results. Preliminary experiments demonstrate that our model can achieve better results in terms of both visual perception and time performance.
Industrial Electronics and Applications (ICIEA), 2011 6th IEEE Conference on; 07/2011
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ABSTRACT: A challenge in virtual reality based laparoscopic cholecystectomy simulation is to construct a fast and accurate deformable gallbladder model. This paper proposes a multi-layer mass-spring model which can adapt well to the built-in accelerating algorithms in PhysX-engine of GPU. The gallbladder is first segmented from clinical CT images. A multi-layer model based on the anatomical structure of gallbladder is subsequently constructed. We configure the parameters of the springs based on the biomechanical properties of gallbladder to ensure the realism of the deformation results. Preliminary experiments demonstrate that our model can achieve satisfactory results in terms of both visual perception and time performance.
Biomedical Engineering and Informatics (BMEI), 2010 3rd International Conference on; 11/2010
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ABSTRACT: Fast bone segmentation is often important in computer-aided medical systems. Thresholding-based techniques have been widely used to identify the object of interest (bone) against dark backgrounds. However, the darker areas that are often present in bone tissue may adversely affect the results obtained using existing thresholding-based segmentation methods. We propose an automatic, fast, robust and accurate method for the segmentation of bone using 3D adaptive thresholding. An initial segmentation is first performed to partition the image into bone and non-bone classes, followed by an iterative process of 3D correlation to update voxel classification. This iterative process significantly improves the thresholding performance. A post-processing step of 3D region growing is used to extract the required bone region. The proposed algorithm can achieve sub-voxel accuracy very rapidly. In our experiments, the segmentation of a CT image set required on average less than 10s per slice. This execution time can be further reduced by optimizing the iterative convergence process.
Computers in biology and medicine 02/2010; 40(2):231-6. · 1.27 Impact Factor
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ABSTRACT: Medical simulators are increasingly being used for surgical training. For interactive surgical simulation involving haptic rendering, the force at the needle tip has to be computed very fast. We are developing biomechanical models for bone needle insertion. The cortical bone can be regarded as a dense form of cancellous bone that can be modeled using a linear elastic material. The porosity of the bone determines the resistance felt as the user inserts the needle into the bone. The bar element method that represents each trabecular bone as a FE beam is most computationally efficient. With 1000 FE elements, the computed force feedback were close to the insertion force measured during experiments. However, the extended bar element method may be the more appropriate choice for taking into consideration the trabecular distribution and hence, inhomogeneous of bone. The simulation studies on bone-needle interaction also showed that a diamond bevel needle may penetrate the bone with less force
Control, Automation, Robotics and Vision, 2006. ICARCV '06. 9th International Conference on; 01/2007
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ABSTRACT: Surface based registration is commonly used in image aided surgery. This technique is extremely computationally expensive due to (1) the number of iterations required to search through the large parameter space and (2) the heavy computational load needed for determining the cost function (the distance between two surfaces). This is the main obstacle in pushing surface based registration for image guided surgery, where near real time registration is needed. Most attempts to reduce the computational burden, e.g., gradient descent and ICP, have been targeted at reducing the number of iterations for the optimization. In this paper, we propose to use a neural network to model the surface of the reference structure. This not only provides an accurate model for the surface but also a fast method for computing the cost function. For CT-CT spine registration, the time taken to register two spine surfaces is about 10 times faster compared to the commonly used triangular mesh modeling with similar registration accuracy.
Biomedical Circuits and Systems, 2004 IEEE International Workshop on; 01/2005
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ABSTRACT: Automatic and accurate multi-modality (CT/MRI) image registration is an important part of image guided surgery, pre-surgery planning and post-surgery evaluation. Surface based registration is commonly used for registration of CT and MRI images of bone. Surface extraction from CT and MRI datasets is a pre-requisite for the registration. It is known that it is not possible to achieve fully automatic, accurate and complete segmentation of the spine from MRI dataset. Thus surface based registration for CT-MRI spine datasets cannot be fully automated. In this paper, we investigate the use of normalized mutual information as a method for fully automatic and accurate registration of CT-MRI spine datasets. We have compared the registration results with those from the surface based registration. Our results are promising and show that normalized mutual information can be used to implement fully automatic and accurate registration for CT and MRI images of the spine.
Biomedical Circuits and Systems, 2004 IEEE International Workshop on; 01/2005
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ABSTRACT: An automatic surface-based rigid registration system using a neural network representation is proposed. The system has been applied to register human bone structures for image-guided surgery. A multilayer perceptron neural network is used to construct a patient-specific surface model from pre-operative images. A surface representation function derived from the resultant neural network model is then employed for intra-operative registration. The optimal transformation parameters are obtained via an optimization process. This segmentation/registration system achieves sub-voxel accuracy comparable to that of conventional techniques, and is significantly faster. These advantages are demonstrated using image datasets of the calcaneus and vertebrae.
Image and Vision Computing.
