Xiangyun Liao

Multi-scale and shape constrained localized region-based active contour segmentation of uterine fibroid ultrasound images in HIFU therapy.

Purpose To overcome the severe intensity inhomogeneity and blurry boundaries in HIFU (High Intensity Focused Ultrasound) ultrasound images, an accurate and efficient multi-scale and shape constrained localized region-based active contour model (MSLCV), was developed to accurately and efficiently segment the target region in HIFU ultrasound images of uterine fibroids. Methods We incorporated a new shape constraint into the localized region-based active contour, which constrained the active contour to obtain the desired, accurate segmentation, avoiding boundary leakage and excessive contraction. Localized region-based active contour modeling is suitable for ultrasound images, but it still cannot acquire satisfactory segmentation for HIFU ultrasound images of uterine fibroids. We improved the localized region-based active contour model by incorporating a shape constraint into region-based level set framework to increase segmentation accuracy. Some improvement measures were proposed to overcome the sensitivity of initialization, and a multi-scale segmentation method was proposed to improve segmentation efficiency. We also designed an adaptive localizing radius size selection function to acquire better segmentation results. Results Experimental results demonstrated that the MSLCV model was significantly more accurate and efficient than conventional methods. The MSLCV model has been quantitatively validated via experiments, obtaining an average of 0.94 for the DSC (Dice similarity coefficient) and 25.16 for the MSSD (mean sum of square distance). Moreover, by using the multi-scale segmentation method, the MSLCV model’s average segmentation time was decreased to approximately 1/8 that of the localized region-based active contour model (the LCV model). Conclusions An accurate and efficient multi-scale and shape constrained localized region-based active contour model was designed for the semi-automatic segmentation of uterine fibroid ultrasound (UFUS) images in HIFU therapy. Compared with other methods, it provided more accurate and more efficient segmentation results that are very close to those obtained from manual segmentation by a specialist.

Parallel computing of 3D smoking simulation based on OpenCL heterogeneous platform

Open Computing Language (OpenCL) is an open royalty-free standard for general purpose parallel programming across Central Processing Units (CPUs), Graphic Processing Units (GPUs) and other processors. This paper introduces OpenCL to implement real-time smoking simulation in a virtual surgery training simulation system. Firstly, the Computational Fluid Dynamics (CFD) is adopted to construct the real-time smoking simulation model based on the Navier–Stokes (N-S) equations of an incompressible fluid under the condition of normal temperature and pressure. Then we propose a parallel computing technique based on OpenCL to accomplish the parallel computing of smoking simulation model on CPU and GPU, respectively. Finally, we render the smoke in real time by using a three-dimensional (3D) texture volume rendering method. Experimental results show that the parallel computing technique we have proposed achieve a satisfactory effect on image quality and rendering rate both on CPU and GPU.

3D soft tissue warping dynamics simulation based on force asynchronous diffusion model

Soft tissue warping is one of the key technologies of medical dynamics simulation, such as surgical simulation, image guided surgery. In this paper, we present a novel simulation method which is stable and fast like linear models for soft tissue warping simulation. This method performs on the irregular mesh models, and it is able to represent the visual properties of physical processes with low computational complexity using the Force Asynchronous Diffusion Model (FADM) proposed in this paper. It contains three parts: model preprocessing, collision detection and simulation model solution. In model preprocessing, we establish three models based on the triangular mesh: the geometrical model, the physical model and the transitional model. A two‐level collision detection algorithm is presented based on the three models. At every time step of the simulation model solution, to more accurately reflect the internal physical properties of the soft tissue, we divide the springs in physical model into three kinds: tissue springs, connection springs and virtual springs; and we propose the asynchronous regions and active regions to simplify the computing process according to the realistic physical warping. Experimental results show the FAMD can achieve good warping effects on speed and realism. Copyright

Coupled tissue bleeding simulation in virtual surgery

Tissue bleeding simulation is the key problem in virtual surgery, which greatly reduces the risk of real surgery. It also plays an important role in the application of fluid-solid coupling. Traditional methods of fluid-solid coupling are almost mesh-based, in this paper we proposed a tissue bleeding model as well as an improved collision detection and response algorithm based on SPH (Smoothed Particle Hydrodynamics). Our tissue bleeding model describes Navier-Stokes equations with SPH method and our tissue model is set as rigid body. Experimental results show that our coupled tissue bleeding simulation model possesses high fidelity and strong robustness.

A Robust Physics-Based 3D Soft Tissue Parameters Estimation Method for Warping Dynamics Simulation

Soft tissue warping is one of the key technologies in dynamic simulation of many surgical procedures. To achieve high performance simulation of 3D soft tissue warping, the research of physical parameters estimation of the warping model is of great significance. Through the construction of parameters estimation platform which consists of an optical tracking system PPT2 (Precision Position Tracker with 2 Cameras) and pressure acquisition devices, we obtain the nodal displacements of tetrahedron finite element model and external forces on it. Then we calculate the parameters of 3D soft tissue by using reverse engineering method and verify the parameters by comparing the calculated nodal displacements and the measured nodal displacements of the soft tissue. The experimental results show that the Physics-based 3D soft tissue parameters estimation method we proposed have achieve accurate agreement of calculated nodal displacements and the measured nodal displacements and it has the properties of accuracy and robust;

Adaptive localised region and edge-based active contour model using shape constraint and sub-global information for uterine fibroid segmentation in ultrasound-guided HIFU therapy

Uterine fibroids segmentation in ultrasound images is of great importance in the definition of intra-operative planning of ultrasound-guided high-intensity focused ultrasound (HIFU) therapy. However, it is challenging to obtain accurate, robust and efficient uterine fibroid segmentation due to low quality of ultrasound images. In this study, the authors propose a novel adaptive localised region and edge-based active contour model using shape constraint and sub-global information to accurately and efficiently segment the uterine fibroids in ultrasound images with robustness against initial contour. The authors first define adaptive local radius for the localised region-based model and combine it with the edge-based model to accurately and efficiently capture images heterogeneous features and edge features. Then, they incorporate a shape constraint to reduce boundary leakage or excessive contraction to obtain more accurate segmentation. To overcome the initialisation sensitivity, they introduce the sub-global information to prevent the curve from trapping into the local minima and obtain robust results. Furthermore, the authors optimise computation by adaptively sharing local region and employing the multi-scale segmentation method to achieve efficient segmentation. The proposed method is validated by uterine fibroid ultrasound images in HIFU therapy and the results demonstrate that it can achieve accurate, robust and efficient segmentation.

GPU-assisted energy asynchronous diffusion parallel computing model for soft tissue deformation simulation

Soft tissue deformation simulation is a key technology of virtual surgical simulation. In this work, we present a graphics processing unit (GPU)-assisted energy asynchronous diffusion parallel computing model which is stable and fast in processing complex models, especially concave surface models. We adopt hexahedral voxels to represent the physical model of soft tissue to improve the visual realistic quality and computing efficiency of deformation simulation. We also adopt the concept of free boundary to simulate soft tissue geometric characteristics more precisely during the deformation process and introduce asynchronous diffusion by using the mechanical energy of mass points to achieve realistic soft tissue deformation effects. In order to meet the requirement of real-time surgery simulation, we accelerate the soft tissue deformation by using OpenCL (Open Computing Language) and optimize the parallel computing process in several means. Experimental results have shown that the GPU-assisted energy asynchronous diffusion parallel computing model for soft tissue deformation simulation implements satisfactory effects on deformation in visual realistic and real-time quality.

An energy-based free boundary asynchronous diffusion model for 3D warping of tissue dynamics

Soft tissue warping is one of the key technologies in dynamic simulation of many surgical procedures, such as image-guided surgery, surgical navigation and structural/lesion localization. Previously, we proposed a force asynchronous diffusion model (FADM) for soft tissue warping simulation. The FADM works well if a lesion or an anatomical object has a convex shape. However, in some cases, this convex assumption is invalid. In order to remove this significant limitation, this paper presents an energy-based free boundary asynchronous diffusion model (EBFBADM) with three unique features. First, we utilize hexahedral voxels to represent the physical model of the surface triangular mesh and a regional proliferation algorithm to remove invalid voxels, simplify computation and improve realness in warping simulation. Second, we adopt the concept of free boundary to simulate soft tissue geometric characteristics more precisely during the warping process. Finally, we optimize the process of asynchronous diffusion by using the mechanical energy of mass point to achieve realistic soft tissue warping effects. Experimental results have shown that the EBFBADM improves performance in both computation and realness in surgical simulation.

Animating Wall-Bounded Turbulent Smoke via Filament-Mesh Particle-Particle Method

Turbulent vortices in smoke flows are crucial for a visually interesting appearance. Unfortunately, it is challenging to efficiently simulate these appealing effects in the framework of vortex filament methods. The vortex filaments in grids scheme allows to efficiently generate turbulent smoke with macroscopic vortical structures, but suffers from the projection-related dissipation, and thus the small-scale vortical structures under grid resolution are hard to capture. In addition, this scheme cannot be applied in wall-bounded turbulent smoke simulation, which requires efficiently handling smoke-obstacle interaction and creating vorticity at the obstacle boundary. To tackle above issues, we propose an effective filament-mesh particle-particle (FMPP) method for fast wall-bounded turbulent smoke simulation with ample details. The Filament-Mesh component approximates the smooth long-range interactions by splatting vortex filaments on grid, solving the Poisson problem with a fast solver, and then interpolating back to smoke particles. The Particle-Particle component introduces smoothed particle hydrodynamics (SPH) turbulence model for particles in the same grid, where interactions between particles cannot be properly captured under grid resolution. Then, we sample the surface of obstacles with boundary particles, allowing the interaction between smoke and obstacle being treated as pressure forces in SPH. Besides, the vortex formation region is defined at the back of obstacles, providing smoke particles flowing by the separation particles with a vorticity force to simulate the subsequent vortex shedding phenomenon. The proposed approach can synthesize the lost small-scale vortical structures and also achieve the smoke-obstacle interaction with vortex shedding at obstacle boundaries in a lightweight manner. The experimental results demonstrate that our FMPP method can achieve more appealing visual effects than vortex filaments in grids scheme by efficiently simulating more vivid thin turbulent features.

Patch green coordinates based interactive embedded deformable model

Virtual surgery is a serious game which provides an opportunity to acquire cognitive and technical surgical skills via virtual surgical training and planning. However, interactively and realistically manipulating the human organ and simulating its motion under interaction is still a challenging task in this field. The underlying reason for this issue is the conflict requirements for physical constraints with high fidelity and real-time performance. To achieve realistic simulation of human organ motion with volume conservation, smooth interpolation under large deformation and precise frictional contact mechanics of global behavior in surgical scenario. This paper presents a novel and effective patch Green coordinates based interpolation for embedded deformable model to achieve the volume-preserving and smooth interpolation effects. Besides, we resolve the frictional contact mechanics for embedded deformable model, and further provide the precise boundary conditions for mechanical solver. In addition, our embedded deformable model is based on the total lagrangian explicit dynamics (TLED) finite element method (FEM) solver, which can well handle the large biological tissue deformation with both nonlinear geometric and material properties. In real compression experiments, our method can achieve liver deformation with average accuracy of 3.02 mm. Besides, the experimental results demonstrate that our method can also achieve smoother interpolation and volume-preserving effects than original embedded deformable model, and allows complex and accurate organ motion with mechanical interactions in virtual surgery.