Modeling fibrous soft tissue dissection with elastic-plastic deformation for simulation of brain tumor removal.

BACKGROUND AND OBJECTIVE: Realistic modeling the dissection of brain tissue is of key importance for simulation of brain tumor removal in virtual neurosurgery systems. However, existing methods are unable to characterize inelastic behaviors of brain tissue, such as plastic deformation and dissection evolution, making it ineffective in simulating brain tumor removal procedures. METHODS: In this paper, a model of fibrous soft tissue dissection for the simulation of brain tumor removal is proposed. A dissection variable of representative volume element is used to characterize the dissection state of the fibrous soft tissue. The evolution of dissection with elastic-plastic deformation under the effects of external loads is presented. RESULTS: Simulation results show that the proposed model provides realistic, stable and intuitive results in the simulation of fracture in fibrous soft tissues. As the external load increases, the fibrous soft tissue begins to crack, with the cracks growing and multiplying until they eventually merge to form a fracture. The proposed model is incorporated into the simulation of brain tumor removal. CONCLUSIONS: The experimental results demonstrate the feasibility of modeling fibrous soft tissue dissection with elastic-plastic deformation. A relative high degree of realistic visual feedback is achieved.

Fully automated cardiac MRI segmentation using dilated residual network.

PURPOSE: Cardiac ventricle segmentation from cine magnetic resonance imaging (CMRI) is a recognized modality for the noninvasive assessment of cardiovascular pathologies. Deep learning based algorithms achieved state-of-the-art result performance from CMRI cardiac ventricle segmentation. However, most approaches received less attention at the bottom layer of UNet, where main features are lost due to pixel degradation. To increase performance, it is important to handle the bottleneck layer of UNet properly. Considering this problem, we enhanced the performance of main features at the bottom layer of network. METHOD: We developed a fully automatic pipeline for segmenting the right ventricle (RV), myocardium (MYO), and left ventricle (LV) by incorporating short-axis CMRI sequence images. We propose a dilated residual network (DRN) to capture the features at full resolution in the bottleneck of UNet. Thus, it significantly increases spatial and temporal information and maintains the localization accuracy. A data-augmentation technique is employed to avoid overfitting and class imbalance problems. Finally, output from each expanding path is added pixel-wise to improve the training response. RESULTS: We used and evaluated our proposed method on automatic cardiac diagnosis challenge (ACDC). The test set consists of 50 patient records. The overall dice similarity coefficient (DSC) we achieved for our model is 0.924 ± 0.03, 0.907 ± 0.01, and 0.949 ± 0.05 for RV, MYO, and LV, respectively. Similarly, we obtained hausdorff distance (HD) scores of 10.09 ± 0.01, 7.25 ± 0.05, and 6.86 ± 0.02 mm for RV, MYO, and LV, respectively. The results show superior performance and outperformed state-of-the-art methods in terms of accuracy and reached expert-level segmentation. Consequently, the overall DSC and HD result improved by 1.0% and 1.5%, respectively. CONCLUSION: We designed a dilated residual UNet (DRN) for cardiac ventricle segmentation using short-axis CMRI. Our method has the advantage of restoring and capturing spatial and temporal information by expanding the receptive field without degrading the image main features in the bottleneck of UNet. Our method is highly accurate and quick, taking 0.28 s on average to process 2D MR images. Also, the network was designed to work on predictions of individual MR images to segment the ventricular region, for which our model outperforms many state-of-the-art methods.

Modeling of glioma growth using modified reaction-diffusion equation on brain MR images.

BACKGROUND AND OBJECTIVE: Modeling of glioma growth and evolution is of key importance for cancer diagnosis, predicting clinical progression and improving treatment outcomes of neurosurgery. However, existing models are unable to characterize spatial variations of the proliferation and infiltration of tumor cells, making it difficult to achieve accurate prediction of tumor growth. METHODS: In this paper, a new growth model of brain tumor using a reaction-diffusion equation on brain magnetic resonance images is proposed. Both the heterogeneity of brain tissue and the density of tumor cells are used to estimate the proliferation and diffusion coefficients of brain tumor cells. The diffusion coefficient that characterizes tumor diffusion and infiltration is calculated based on the ratio of tissues (white and gray matter), while the proliferation coefficient is evaluated using the spatial gradient of tumor cells. In addition, a parameter space is constructed using inverse distance weighted interpolation to describe the spatial distribution of proliferation coefficient. RESULTS: The glioma growth predicted by the proposed model were tested by comparing with the real magnetic resonance images of the patients. Experiments and simulation results show that the proposed method achieves accurate modeling of glioma growth. The interpolation-based growth model has higher average dice score of 0.0647 and 0.0545, and higher average Jaccard index of 0.0673 and 0.0573, respectively, compared to the uniform- and gradient-based growth models. CONCLUSIONS: The experimental results demonstrate the feasibility of calculating the proliferation and diffusion coefficients of the growth model based on patient-specific anatomy. The parameter space that characterizes spatial distribution of proliferation and diffusion coefficients is established and incorporated into the simulation of glioma growth. It enables to obtain patient-specific models about glioma growth by estimating and calibrating only a few model parameters.

A deformation model of pulsating brain tissue for neurosurgery simulation.

BACKGROUND AND OBJECTIVES: For neurological simulation, an accurate deformation model of brain tissue is of key importance for faithful visual feedback. Existing models, however, do not take into account intracranial pulsation, which degrades significantly the realism of visual feedback. METHODS: In this paper, a finite element model incorporating intracranial pressure is proposed for simulating brain tissue deformation with pulsation. An implicit Euler method is developed to calculate the deformation of brain tissue. A circuit model of intracranial pressure dynamics is established based on cerebral blood and cerebrospinal fluid circulations. The intracranial pulsation of pressure is introduced into the deformation model, so that the simulated brain tissues pulsate with a rhythm in accord with the changes of intracranial pressure, which resembles real-life neurosurgery. RESULTS AND CONCLUSIONS: The experimental implementation of the proposed deformation model and the calculation method shows that it provides realistic simulation of brain tissue pulsation and real-time performance is achieved on an ordinary computer for certain procedures of neurosurgery.

Modeling of connective tissue damage for blunt dissection of brain tumor in neurosurgery simulation.

We introduce a new model for connective tissue damage in blunt dissection, which is a very important process in neurosurgery simulation. Specifically, the tool-tissue interaction between the instrument and connective tissue is incorporated into the model of connective tissue damage. This damage develops with the evolution criterion due to the effect of the external load. The tetrahedral mesh in the soft tissue model is removed for the representation of rupture as the damage accumulates to the threshold value. Analysis and experiments show that the connective tissue damage model provides stable, visually realistic results for the simulation of the connective tissue rupture process. The stiffness of the connective tissue decreases as the damage accumulates. The proposed model for connective tissue damage was incorporated into the development of a neurosurgery simulator, in which blunt dissection of a brain tumor was simulated.

A new model of soft tissue with constraints for interactive surgical simulation.

BACKGROUND AND OBJECTIVES: An accurate and real-time model of soft tissue is critical for surgical simulation for which a user interacts haptically and visually with simulated patients. This paper focuses on the real-time deformation model of brain tissue for the interactive surgical simulation, such as neurosurgical simulation. METHODS: A new Finite Element Method (FEM) based model with constraints is proposed for the brain tissue in neurosurgical simulation. A new energy function of constraints characterizing the interaction between the virtual instrument and the soft tissue is incorporated into the optimization problem derived from the implicit integration scheme. Distance and permanent deformation constraints are introduced to describe the interaction in the convexity meningioma dissection and hemostasis. The proposed model is particularly suitable for GPU-based computing, making it possible to achieve real-time performance. RESULTS AND CONCLUSIONS: Simulation results show that the simulated soft tissue exhibits the behaviors of adhesion and permanent deformation under the constraints. Experiments show that the proposed model is able to converge to the exact solution of the implicit Euler method after 96 iterations. The proposed model was implemented in the development of a neurosurgical simulator, in which surgical procedures such as dissection of convexity meningioma and hemostasis were simulated.