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The microstructure of metallic materials plays a crucial role in determining their performance. In order to accurately predict the dynamic recrystallization (DRX) behavior and microstructural evolution during the hot deformation process of GCr15 bearing steel, a microstructural evolution model for the DRX process of GCr15 steel was established by combining the level set (LS) method with the Yoshie–Laasraoui–Jonas dislocation dynamics model. Firstly, hot compression tests were conducted on GCr15 steel using the Gleeble-1500D thermal simulator, and the hardening coefficient k1 and dynamic recovery coefficient k2 of the Yoshie–Laasraoui–Jonas model were derived from the experimental flow stress data. The effects of temperature, strain, and strain rate on DRX behavior and grain size during the hot working process of GCr15 steel were investigated. Through secondary development of the software, the established microstructural evolution model was integrated into the DIGIMU® software. Metallographic images were imported in situ to reconstruct its initial microstructure, enabling GCr15 steel DRX microstructure finite element simulation of the hot compression process. The predicted mean grain size and flow stress demonstrated a strong correlation and excellent agreement with the experimental results. The results demonstrate that the established DRX model effectively predicts the evolution of the DRX fraction and average grain size during the hot forging process and reliably forecasts DRX behavior. [ABSTRACT FROM AUTHOR]

Purpose: Although many conventional level-set approaches can be used for segmenting images containing factors such as noise and intensity inhomogeneities, they still can impact the accuracy of the results seriously. To solve this problem, a level-set method for fast image segmentation based on pre-fitting and bilateral filtering is proposed. Design/methodology/approach: Firstly, an improved bilateral filter was investigated for image preprocessing. Secondly, by computing the local average intensity of the preprocessed enhanced picture, two local pre-fitting functions were defined. Thirdly, a new level-set energy functional was defined. Finally, a new distance regularized energy term based on the logarithmic and polynomial functions is proposed to evolve the level-set function in a smooth state. Findings: The experimental results demonstrate that the proposed model has an excellent segmentation capability for images with noise and intensity inhomogeneities and has different degrees of performance improvement compared with the mainstream models. Originality/value: (C1) An improved bilateral filter was investigated and integrated into the model. (C2) Proposing two local pre-fitting functions by computing the local average intensity of the preprocessed enhanced image. (C3) Proposing a new level-set energy functional. (C4) A new distance regularized energy term based on the logarithmic and polynomial functions is proposed to evolve the level set function in a smooth state. (C5) Analyzing and comparing the performance of the proposed model with other similar models. [ABSTRACT FROM AUTHOR]

This paper introduces a multiphysical model for laser welding in a lap joint configuration with dissimilar metals. Initially solved in a 2D axisymmetric configuration for static shots, the model is extended to 3D to simulate laser welding with a weld seam formation. Heat transfer, fluid flow, and species tracking are solved with the level set method to describe dynamically the keyhole and melt pool behavior. Validation against experimental data shows an accurate description of the main phenomena. The paper mainly highlights the need of introducing a thermal contact resistance to correctly predict the melted area dimensions. The study emphasizes the importance of considering imperfect material contact and proposes an effective approach for thermal contact resistance, a phenomenon poorly discussed in the literature. [ABSTRACT FROM AUTHOR]

This study introduces a deep self-organizing map neural network based on level-set (LS-SOM) for the customization of a shoe-last defined from plantar pressure imaging data. To alleviate the over-segmentation problem of images, which refers to segmenting images into more subcomponents, a domain-based segmentation model of plantar pressure images was constructed. The domain growth algorithm was subsequently modified by optimizing its parameters. A SOM with 10, 15, 20, and 30 hidden layers was compared and validated according to domain growth characteristics by using merging and splitting algorithms. Furthermore, we incorporated a level set segmentation method into the plantar pressure image algorithm to enhance its efficiency. Compared to the literature, this proposed method has significantly improved pixel accuracy, average cross-combination ratio, frequency-weighted cross-combination ratio, and boundary F1 index comparison. Using the proposed methods, shoe lasts can be designed optimally, and wearing comfort is enhanced, particularly for people with high blood pressure. [ABSTRACT FROM AUTHOR]

As a method of lightweight design, multi-material design aims to make targeted use of materials in order to reduce CO2 emissions. In this context, it can be described as one of the product development methods used to meet the challenges of climate change. However, since the design of structures in multi-material design is complex, topology optimization can be used to support the product developer. In this article, a multi-material topology optimization method is developed that combines the Velocity Field Level Set method with the Reconciled Level Set method. Furthermore, the current design is explicitly resolved in each iteration by means of multi-material remeshing. The edge collapse phase in the remeshing process is achieved by applying the producer consumer pattern. The developed method is then validated using known examples from the state of research, and the influence of the parameters of the method on the result is analyzed by means of studies. [ABSTRACT FROM AUTHOR]

We propose a method to modify a polygonal mesh in order to fit the zero-isoline of a level set function by extending a standard body-fitted strategy to a tessellation with arbitrarily-shaped elements. The novel level set-fitted approach, in combination with a Discontinuous Galerkin finite element approximation, provides an ideal setting to model physical problems characterized by embedded or evolving complex geometries, since it allows us skipping any mesh post-processing in terms of grid quality. The proposed methodology is firstly assessed on the linear elasticity equation, by verifying the approximation capability of the level set-fitted approach when dealing with configurations with heterogeneous material properties. Successively, we combine the level set-fitted methodology with a minimum compliance topology optimization technique, in order to deliver optimized layouts exhibiting crisp boundaries and reliable mechanical performances. An extensive numerical test campaign confirms the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The mesoscopic pores of double-porosity media are usually designed to enhance the pressure diffusion effect to improve sound absorption. In this paper, a topology optimization model with a parametric level set method for double-porosity structures is developed to design the distribution of mesoscopic pores for maximizing sound absorption. The sound propagation in poroelastic media is described by the Biot-Allard theory, and the sound absorption is obtained by the power balance approach. Structure boundaries during optimization are traced by a parametric level set method. A distance regularization term is added to the objective function to ensure the numerical stability. Numerical examples in 2D waveguides are given to verify the model, and the effects of initial designs and distance regularization on the optimized configuration are discussed. We find that with the target over a wide frequency range, the strengthening of frame elastic effect during the optimization process is accurately described by the power balance approach and demonstrates the advantage of the poroelastic model for topology optimization over the equivalent fluid model. [ABSTRACT FROM AUTHOR]

The accurate assessment of peanut seed plumpness is crucial for optimizing peanut production and quality. The current method is mainly manual and visual inspection, which is very time-consuming and causes seed deterioration. A novel imaging technique is used to enhance the detection of peanut seed fullness using a non-destructive soft X-ray, which is suitable for the analysis of the surface or a thin layer of a material. The overall grayscale of the peanut is similar to the background, and the edge of the peanut seed is blurred. The inaccuracy of peanut overall and peanut seed segmentation leads to low accuracy of seed plumpness detection. To improve accuracy in detecting the fullness of peanut seeds, a seed plumpness detection method based on level set and multi-threshold segmentation was proposed for peanut images. Firstly, the level set algorithm is used to extract the overall contour of peanuts. Secondly, the obtained binary image is processed by morphology to obtain the peanut pods (the peanut overall). Then, the multi-threshold OTSU algorithm is used for threshold segmentation. The threshold is selected to extract the peanut seed part. Finally, morphology is used to complete the cavity to achieve the segmentation of the peanut seed. Compared with optimization algorithms, in the segmentation of the peanut pods, average random index (RI), global consistency error (GCE) and variation of information (VI) were increased by 10.12% and decreased by 0.53% and 24.11%, respectively. Compared with existing algorithms, in the segmentation of the peanut seed, the average RI, VI and GCE were increased by 18.32% and decreased by 9.14% and 6.11%, respectively. The proposed method is stable, accurate and can meet the requirements of peanut image plumpness detection. It provides a feasible technical means and reference for scientific experimental breeding and testing grading service pricing. [ABSTRACT FROM AUTHOR]

Lung segmentation is a prerequisite for lung cancer diagnosis with computer-aided diagnosis systems. However, correct lung segmentation is a challenging task due to image noise, diseases, different lung nodule presences, unique morphological variations, and other factors. In this study, we present a novel algorithm for lung segmentation of thoracic Computed Tomography (CT) images based on a dilated U-Net model and a multi-scale gray correlation-based approach. Lung regions were first extracted from CT images with a double dilated U-Net model for generating accurate lung contours with juxta-pleural nodules included. Then, initial nodule contours were captured using a novel multi-scale gray correlation-based segmentation approach for reducing the computational burden and improving lung segmentation accuracy in lung nodule segmenting. Finally, lung nodule contours were refined with a level set method. A collection of thoracic CT scans with nodules from two public databases are employed for algorithm testing. Experimental results show that the proposed algorithm creates an average Dice similarity coefficient of 72.14% compared with ground truth, and it also outperforms a number of existing lung segmentation techniques. The accurate lung segmentations generated by the proposed algorithm are helpful for assisting radiologists in evaluating lung nodules and subsequently developing focused treatment strategies. [ABSTRACT FROM AUTHOR]

In this work, we present a two‐dimensional multimaterial arbitrary Lagrangian–Eulerian (ALE) method for simulating compressible flows in which a novel coupled volume of fluid and level set interface reconstruction (VOSET) method is developed for interface capturing. The VOSET method combines the merits of both the volume of fluid method and the level set method by using a geometrical iterative operation. Compared to the original VOSET method, the novel VOSET method proposed in this work further improves the accuracy and fidelity in interface reconstruction procedure, especially in under‐resolved regions. Several typical two‐dimensional numerical experiments are presented to investigate the effectiveness of the proposed VOSET method and its performance when coupling with the multimaterial ALE solver. Numerical results demonstrate its good capability in capturing material interfaces during the simulation of compressible two‐material flows. [ABSTRACT FROM AUTHOR]

The integration of phase change phenomena through an interface is a numerical challenge that requires proper attention. Solutions to properly ensure mass and energy conservation were developed for finite difference and finite volume methods, but not for Finite Element methods. We propose a Finite Element phase change model based on an Eulerian framework with a Continuous Surface Force (CSF) approach. It handles both momentum and energy conservation at the interface for anisotropic meshes in a light an efficient way. To do so, a model based on the Level Set method is developed. A thick interface is considered to fit with the CSF approach. To properly compute the energy conservation, heat fluxes are extended through this interface thanks to the resolution of a transport equation. A dedicated pseudo compressible Navier–Stokes solver is added to compute velocity jumps with a source term at the interface in the velocity divergence equation. Several 1D and 2D benchmarks are considered with increasing complexity to highlight the performances of each feature of the framework. This stresses the capacity of the model to properly tackle phase change problems. [ABSTRACT FROM AUTHOR]

The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has taken on a significant and increasing role in diagnostic procedures and treatments for patients who suffer from chronic kidney disease. Careful segmentation of kidneys from DCE-MRI scans is an essential early step towards the evaluation of kidney function. Recently, deep convolutional neural networks have increased in popularity in medical image segmentation. To this end, in this paper, we propose a new and fully automated two-phase approach that integrates convolutional neural networks and level set methods to delimit kidneys in DCE-MRI scans. We first develop two convolutional neural networks that rely on the U-Net structure (UNT) to predict a kidney probability map for DCE-MRI scans. Then, to leverage the segmentation performance, the pixel-wise kidney probability map predicted from the deep model is exploited with the shape prior information in a level set method to guide the contour evolution towards the target kidney. Real DCE-MRI datasets of 45 subjects are used for training, validating, and testing the proposed approach. The valuation results demonstrate the high performance of the two-phase approach, achieving a Dice similarity coefficient of 0.95 ± 0.02 and intersection over union of 0.91 ± 0.03, and 1.54 ± 1.6 considering a 95% Hausdorff distance. Our intensive experiments confirm the potential and effectiveness of that approach over both UNT models and numerous recent level set-based methods. [ABSTRACT FROM AUTHOR]

One canonical practice in the development and application of level set methods is to convect a shape represented by zero level set with a given, reversible, and periodic velocity field and test how well the original shape is recovered after each cycle. In this short letter, we mathematically show that Crank–Nicolson time integration, combined with standard Galerkin finite element, can exactly recover the original shape after each cycle, regardless of spatiotemporal resolution. This surprising finding is also numerically demonstrated by LeVeque's problem. [ABSTRACT FROM AUTHOR]

Digital transformation has brought radical changes in several domains. Particularly, image processing techniques have been generally used in medical, security, and monitoring applications. Image segmentation is a specific task where an image is partitioned in meaningful segments, containing similar features and properties. Its aim is to simplify the original image for easy analysis since relevant information is highlighted. These techniques are commonly used to support medical experts in detecting areas of interest in medical images. Level set method is a methodology for image segmentation, which works with minimizing energy for segmentation of the image by active contours. The areas inside each contour belong to distinct segments. In active contour-based models, the level of each contour changes according to the intensity values (region-based active contours) or the gradient variations (edge-based active contours). Here, a new edge–region level set algorithm for image segmentation is proposed which controls the curve movement based on both intensity and gradient values. Moreover, the original active contour model has been modified by considering both the mean and the variance values of the pixels' neighborhood, instead of the mean value only. Indeed, in homogeneous regions with the same mean value could be assigned to the same segment while belonging to different ones. Since the initial curve definition is crucial for level set methods, a new methodology for initial curve detection based on Canny edge detector has been proposed. Experiments have been conducted on brain tumor magnetic resonance imaging (MRI). Images from Whole Brain Atlas (Harvard University Medical School) datasets, part Neoplastic Disease (brain tumor) have been used. Results have shown that the suggested approach is able to accurately detect tumor regions in the images and to overcome the original active contour models such as CV, LBF, and LIF. Using semi-average filter in pre-processing stage can strengthen edges and it led to detecting more strong edges in Canny edge detector. [ABSTRACT FROM AUTHOR]

The dehiscence of the Xanthoceras sorbifolium (X. sorbifolium) may lead to seeds jump out and economic loss. The shape and the degree of the crack will provide the relevant elements for the study of the X. sorbifolium dehiscence and duly picked. Therefore, an improved level set method is proposed for X. sorbifolium crack segmentation. The problems of intensity inhomogeneity and so on, which pose challenges for accurate crack segmentation. The local Gaussian distribution fitting method has a good segmentation effect, but it is sensitive to the initial contour and does not use gradient information, which affects the accurate location of the edge. Aiming at the above problems and the scene of crack segmentation, this paper firstly adopts histogram threshold method to obtain the initial contour automatically. Secondly, the energy function is constructed by combining local and edge information. Finally, the double‐well potential function is used to reduce the oscillation and distortion of the method. In this paper, the experiment results show that the average boundary precision is 88.25% and average segmentation time of each image is 12.7s of the proposed method. After comprehensive analysis of IoU and boundary recall, the method in this paper achieves better results. [ABSTRACT FROM AUTHOR]

Microstructural modelling that captures realistic features plays a key role in advancing our knowledge of concrete properties. This paper proposes a computer model based on the level set method to simulate the phase transformation during cement hydration. The model uses the level set function to reconstruct a realistic microstructure of the cement paste, specifically the fibrous form of C-S-H. The dissolution of binder phases as well as the formation of product phases such as inner and outer C-S-H, and portlandite including reaction kinetics are simultaneously reproduced. This allows the quantification of the volumetric phase changes, including chemical shrinkage as air voids. Variation of model parameters allows the investigation of geometrical interactions between phases, such as binder phases and hydrated products. The comparison between two-dimensional simulation and experimental data demonstrates that the level set based model is a promising method for modelling the microstructural evolution during cement hydration. • A computer model based on a level set method for mesoscale cement hydration. • The model differentiates C-S-H into fibrous and dense morphology. • Capturing geometrical interaction between phases. • Quantitative data on evolution of phase fractions including pore size distribution. [ABSTRACT FROM AUTHOR]

To improve the single wafer process for wafer edge yield performance, the liquid film thickness is the key indicator to be investigated. The traditional measurement of liquid film thickness is difficult to execute, due to wafer spinning. The numerical method becomes essential to simulate the process. This paper uses the multiple flow level set method to simulate single wafer spin process. An inlet hydraulic jump is approximated by a two-dimensional axisymmetric approach with a collar inlet. Various spin rotation speeds, variable flow rates, different liquid types and contact angles are simulated. The results indicate liquid film discontinuity at a certain radius and a lower shear rate at the wafer edge. After modifying the process recipe based on the level set simulation result, the wafer edge yield shows improvement. This level set simulation model provides useful information for single wafer wet process control. The level set simulation results can estimate the liquid distribution on the wafer surface before the real process and can avoid experiments on production wafers and hence lead to a lower cost. [ABSTRACT FROM AUTHOR]

BACKGROUND: Kidney CT image with poor quality shows similar gray scale to that of surrounding tissues on abdominal CT images. Therefore, it is difficult to segment the kidney accurately by traditional image segmentation method. OBJECTIVE: To assist the diagnosis of renal diseases and improve the accuracy of renal segmentation in CT images based on an improved geodesic active contour model. METHODS: Based on the comparative analysis of various traditional medical image segmentation algorithms, a kidney segmentation algorithm based on the improved geodesic active contour model was designed. The region of interest was delineated according to prior knowledge, and the initial contour of the kidney was obtained during the pretreatment stage. Based on the geodesic active contour model of the level set method, the boundary response of the kidney region was enhanced and the improved edge indicator function was used to make the contour curve evolution result closer to the real target boundary. RESULTS AND CONCLUSION: The mean Dice coefficient and mean overlap degree of 328 two-dimensional CT images of the kidney were 0.974 9 and 0.907 1, respectively, which were improved compared with other level set methods. Experimental results show that this model can improve the segmentation accuracy and efficiency of the kidney region in abdominal CT images. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Three-dimensional subcooled flow boiling of R134a in a horizontal tube was simulated by a VOF (volume of fluid) model combined with the level set method. Bubble characteristics were explored at heat flux of 0.3 MW/m2, inlet subcooling of 3 K, and inlet velocity of 0.4 m/s. It was observed that five representative bubbles occurred in subcooled flow boiling, including sliding bubble, coalescing bubble, non-departed bubble, bouncing bubble, and continuous-boiling bubble. The results showed that the bubble radial velocity was an important factor of bubble departure after a sliding process. Moreover, the effect of heat flux, inlet velocity, and inlet subcooling on bubble departure diameter were investigated. The departure diameter increased with increasing inlet velocity from 0.2 to 0.4 m/s and heat flux from 0.2 to 0.4 MW/m2, while diameter decreased with inlet subcooling from 3 to 10 K. Finally, based on the influence of heat flux, inlet velocity, and inlet subcooling on average departure diameter of the bubble except the coalescing bubble, a model was proposed to predict the average departure diameter. The deviation of the model was within 5%. [ABSTRACT FROM AUTHOR]

In this paper, we design a Co-flow microchannel capillary device to numerically simulate the generation of double-emulsion droplets by a level set method. The three velocity vortices generated during the generation of double-emulsion droplets and the pressure variation on the z-axis were systematically investigated. The effects of the dimensionless numbers Cain, Caout, and Wein on the volumes of the inner droplets and outer droplets are discussed. The results show that three velocity vortices can be generated during the generation of double-emulsion droplets due to the inhomogeneous force on the droplets. The internal pressure of the inner droplet is much larger than the internal pressure of the outer droplet, and the larger the volume of the droplet the smaller the internal pressure of the droplet. With the increase in the Cain, the volume of the outer droplet increases and the volume of the inner droplet increases and then decreases. As the Caout increases, the volume of the outer and inner droplets decreases significantly. With the increase in the Wein, the volume of the inner droplet increases significantly, and the volume of the outer droplet increases and then decreases. In view of the above phenomenon that the volume of droplet increases and then decreases, we can conclude that the two factors, velocity and pressure, affect the volume of droplet at the same time. [ABSTRACT FROM AUTHOR]

Copyright of Railway Investigation & Surveying is the property of Railway Investigation & Surveying Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Drainage pipe system is the requisite component of the traffic tunnels in Karst area. To reveal the dynamic process of crystallization blockage in drainage pipes, a novel hydrodynamics and hydrochemistry coupled simulation model was developed for calculating the deposition rate of CaCO3 fouling in pipeline surface. Sediments adhering to the pipe walls involve a deformable domain with moving geometric boundaries, and moving mesh and level set methods are proposed for simulation of for tunnel turbulence and crystallization fouling process. The simulation results are compared with the experimental results showing similar trend. The effects of temperature, flow velocity, and solution concentration on crystallization blockage were analyzed by comparative simulation studies. The simulation results show that: (1) the moving mesh method simulated nozzle shrinkage caused by crystalline deposition, without accounting for geometric topology shape changes. However, the level set method tracked the moving topology and thus can simulate the process of complete blockage; (2) the flow velocity in the longitudinal pipe generally exceeded that in the transverse pipe, and the CaCO3 crystal concentration in the transverse pipe eclipsed that in the longitudinal pipe, which meant crystallization blockages primarily occurred in the transverse pipe; (3) the temperature and concentration correlated positively with the crystallization rate, while the crystal precipitation value decreases with the increasing of inlet flow velocity increases. This study advances a hydrodynamics and hydrochemistry coupled crystallization blockage model to provide technical support for the early identification of crystallization-induced pipe blockage in the drainage system in karst tunnel sites. [ABSTRACT FROM AUTHOR]

In this paper we consider a level set reinitialization technique based on a high-order, local discontinuous Galerkin method on unstructured triangular meshes. A finite volume based subcell stabilization is used to improve the nonlinear stability of the method. Instead of the standard hyperbolic level set reinitialization, the flow of time Eikonal equation is discretized to construct an approximate signed distance function. Using the Eikonal equation removes the regularization parameter in the standard approach which allows more predictable behavior and faster convergence speeds around the interface. This makes our approach very efficient especially for banded level set formulations. A set of numerical experiments including both smooth and non-smooth interfaces indicate that the method experimentally achieves design order accuracy. [ABSTRACT FROM AUTHOR]

• Coupled VOF and level set method was applied in jet break-up analysis using adaptive mesh. • Good agreement was obtained in the validation with data of DEFOR-M test. • The characteristics of jet breakup were analyzed under different conditions. • The structure (IGTs and CRGTs) effect and multi-jet effect were investigated. • A new correlation for the jet breakup length was developed. During severe core meltdown accidents of a light water reactor (LWR), the core melt (molten corium) may fall into a water pool, resulting in molten fuel coolant interactions (FCI). Quantitative understanding of FCI phenomena is paramount to corium risk assessment of LWRs such as Nordic boiling water reactors which employ reactor cavity flooding as severe accident management strategy (SAMS). Melt jet breakup and droplet fragmentation play an important role in FCI, affecting debris coolability and steam explosion energetics which are considered in ex-vessel corium risk assessment. The present study is concerned with numerical simulation of melt jet breakup in a water pool using a multiphase computational fluid dynamics (MCFD) approach where a coupled Level Set and Volume of Fluid (CLSVOF) method is used to capture melt-coolant interfaces. The focus is placed on the prediction of interface instabilities and jet breakup length, and their influential factors (melt materials, jet diameter, fall height, in-pool structures, multiple jets and pitch/diameter ratio). The simulation results are compared with the data of the DEFOR-M tests carried out at KTH. There is a good agreement between simulation and experiment, in terms of jet deformation pattern and jet breakup length. It is also found that the jet breakup length is different from the values predicted by well-known correlations (e.g., Taylor's, Epstein Fauske's and Matsuo's). Based on the experimental and numerical data, a new correlation for the jet breakup length is developed in the similar formula of the Satio's correlation. [ABSTRACT FROM AUTHOR]

Level set method has been extensively used for image segmentation, which is a key technology of water extraction. However, one of the problems of the level-set method is how to find the appropriate initial surface parameters, which will affect the accuracy and speed of level set evolution. Recently, the semantic segmentation based on deep learning has opened the exciting research possibilities. In addition, the Convolutional Neural Network (CNN) has shown a strong feature representation capability. Therefore, in this paper, the CNN method is used to obtain the initial SAR image segmentation map to provide deep a priori information for the zero-level set curve, which only needs to describe the general outline of the water body, rather than the accurate edges. Compared with the traditional circular and rectangular zero-level set initialization method, this method can converge to the edge of the water body faster and more precisely; it will not fall into the local minimum value and be able to obtain accurate segmentation results. The effectiveness of the proposed method is demonstrated by the experimental results of flood disaster monitoring in South China in 2020. [ABSTRACT FROM AUTHOR]

A super-layer deposition method is developed for 3D macroscopic finite element modeling of heat transfer at part scale during the powder bed fusion (PBF) process. The proposed super-layer strategy consists of the deposition of batches of several layers. The main consideration is to deal with the effective heating times and with the inter-layer dwell time in a reasonable way. The material is deposited at once for each super-layer thanks to level set and mesh adaptation methods, while the energy input is prescribed, either by respecting the layer-by-layer thermal cycle or in a single thermal load. The level set method is used twice: first to track the interface between gas and the successive super-layers of powder bed and second to track the interface between the part in construction and the nonexposed powder. To preserve simulation accuracy, adaptive remeshing is used to maintain a fine mesh near the evolving construction front during the process. Simulation results obtained by means of this super-layer method are presented and discussed by comparison with those obtained by layer-by-layer strategy, considered here as a reference. It is shown that, when respecting certain conditions, temperature evolutions and distributions approaching the reference ones can be obtained with significant savings on computation time. Assessment is first performed on simple part, then on a more complex configuration. [ABSTRACT FROM AUTHOR]

The launch of synchronous induction coilgun (SICG) is always accompanied by strong thermal and structural effects, which will cause serious damage to the structure of the armature, and multiphysics computation is an effective means to avoid this problem. Compared with the conventional circuit-based model, the finite-element analysis (FEA) is more suitable for the multiphysics computation of SICG, but it is also more time-consuming. In this article, the implicit function and level set (IFLS) method is introduced to rapidly simulate the multiphysics problem of SIGC based on FEA. The armature is modeled implicitly in the material domain and the position of it is updated through the implicit function, avoiding the moving mesh and remesh problems in the arbitrary Lagrangian–Eulerian (ALE) method, which is generally used to dealing with the dynamic mesh problem. Besides, the volume loss density and the Lorentz force per unit in the moving band are transformed back to the initial position to complete the coupling of the thermal and structural field and maintain the accuracy and continuousness of the simulation. A single-stage SICG model is established with the ALE and IFLS methods, and the results show that the computation time of the IFLS method is nearly halved. Finally, the IFLS method is applied to the simulation of multistage SICG, and the computational efficiency is greatly improved. [ABSTRACT FROM AUTHOR]

• A mathematically stringent fiber path optimization method under the level set framework is developed. • The gap/overlap free fiber paths are represented with ISO-value level set profiles. • The sensitivity result in complete boundary integral expression is obtained facilitated by structural skeletons and rays. • A novel adaptive level set adjustment (ALSA) strategy is employed to enhance the robustness of the proposed algorithm. This paper presents a mathematically stringent method for gap/overlap free design of carbon fiber-reinforced composites for 3D printing. The proposed method employs signed distance level set function to represent the fiber paths by extracting the equidistant iso-level set profiles, through which neighboring fiber paths keep a constant distance, eliminating the gap and overlap defects of existing methods that generate fiber paths from discretely defined fiber angle variables. Then, a compliance minimization fiber path optimization problem is formulated under the level set framework for optimal fiber-reinforcement design. A major contribution lies in the solution of the optimization problem. The adjoint sensitivity analysis is performed to derive the gradient information for fiber path design update. More importantly, the domain integral term inside the sensitivity result is transformed into a boundary integral expression facilitated by extraction of the structural skeletons and rays. Hence, the full sensitivity result is derived instead of eliminating the non-executable domain integral terms in existing studies, which results in even faster and more stable convergence. A few numerical examples are studied to demonstrate the effectiveness of the proposed fiber path optimization method. 3D printing and mechanical testing results validate the optimized fiber-reinforcement effect, as well as the manufacturability improvement by avoiding gap and overlap defects. [ABSTRACT FROM AUTHOR]

An approach for optimal control of the interface between water and ferrofluid in a 2-D two-phase flow is proposed in the presence of a magnetic field generated by a matrix of driving electromagnets. First, a model combining Navier–Stokes equations and level set methods is developed. Since it is very computationally demanding, an approximate black-box model based on neural networks replacing the original model is constructed for the purpose of control design. In particular, one-hidden-layer feedforward neural networks with a different number of neurons are trained to predict the water–ferrofluid behavior with accuracy. Then, optimal control based on such black-box models is addressed by selecting the currents flowing in the electromagnets that minimize a cost function given by the symmetric difference between the desired shape and the actual interface separating water and ferrofluid. Numerical results based on both simulation and experimental data collected on the field showcase the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Segmentation of the endocardial and epicardial boundaries on 3D cardiac magnetic resonance images plays a vital role in the assessment of ejection fraction, wall thickness, end-diastolic volume, end-systolic volume, and stroke volume. Accurate segmentation is significantly challenged by intensity inhomogeneity artifacts, low contrast, and ill-defined organ/region boundaries. We propose a two stage hybrid active contour model for robust left ventricle (LV) segmentation accompanied with a new initialization technique based on prior of the LV structure. The proposed approach includes a new level set method using local, spatially-varying, statistical model for image intensity, an edge-based term to capture region boundaries, and regularization functionals for smooth evolution of the segmenting curve and to avoid expensive reinitialization. Moreover, convex hull interpolation is employed to include the papillary muscles within the endocardial boundary for a refined depiction of LV geometry. The accuracy and robustness of the proposed algorithm were assessed using York, Sunnybrook and ACDC datasets (33 + 45 + 100 subjects), with a wide spectrum of normal hearts, congenital heart diseases, and cardiac dysfunction. Experiments showed that the proposed approach significantly outperformed other active contour methods (overall Dice score 0.90), generating accurate segmentations of left ventricular outflow tract (Dice score 0.91), apical slices (Dice score 0.82), systolic and diastolic phases (Dice scores 0.92 and 0.88 respectively). The percentage of good contours was about 92% and the average perpendicular distance was less than 1.8 mm. Automatically generated segmentation yielded superior estimates of ejection fraction with an R2 ≥ 0.937. Furthermore, the proposed method was validated using 100 cine MRI cases consisting of five different cardiac classes from the ACDC MICCAI 2017 challenge. The proposed algorithm yielded superior segmentation performance compared with existing active contour models and other state-of-the-art cardiac segmentation techniques, with extensive validation on three standard cardiac datasets, with different cardiac pathologies and phases. [ABSTRACT FROM AUTHOR]

We present a framework for real-time 3D reconstruction of non-rigidly moving surfaces captured with a single RGB-D camera. Based on the variational level set method, it warps a given truncated signed distance field (TSDF) to a target TSDF via gradient flow without explicit correspondence search. We optimize an energy that contains a data term which steers towards voxel-wise alignment. To ensure geometrically consistent reconstructions, we develop and compare different strategies, namely an approximately Killing vector field regularizer, gradient flow in Sobolev space and newly devised accelerated optimization. The underlying TSDF evolution makes our approach capable of capturing rapid motions, topological changes and interacting agents, but entails loss of data association. To recover correspondences, we propose to utilize the lowest-frequency Laplacian eigenfunctions of the TSDFs, which encode inherent deformation patterns. For moderate motions we are able to obtain implicit associations via a term that imposes voxel-wise eigenfunction alignment. This is not sufficient for larger motions, so we explicitly estimate voxel correspondences via signature matching of lower-dimensional eigenfunction embeddings. We carry out qualitative and quantitative evaluation of our geometric reconstruction fidelity and voxel correspondence accuracy, demonstrating advantages over related techniques in handling topological changes and fast motions. [ABSTRACT FROM AUTHOR]

Active contour models have been widely used in image segmentation, and the level set method (LSM) is the most popular approach for solving the models, via implicitly representing the contour by a level set function. However, the LSM suffers from high computational burden and numerical instability, requiring additional regularization terms or reinitialization techniques. In this paper, we use characteristic functions to implicitly represent the contours, propose a new representation to the geodesic active contours, and derive an efficient algorithm termed the iterative convolutionthresholding method (ICTM). Compared to the LSM, the ICTM is simpler and much more efficient. In addition, the ICTM enjoys most desired features of the level set--based methods. Extensive experiments, on two-dimensional (2D) synthetic, 2D ultrasound, 3D computed tomography, and 3D magnetic resonance images for nodule, organ, and lesion segmentation demonstrate that the proposed method not only obtains comparable or even better segmentation results (compared to the LSM) but also achieves significant acceleration. [ABSTRACT FROM AUTHOR]

Electrocoalescence is an industrial application of high voltage for separating an aqueous phase from an oil phase using electric field to promote the aggregation of aqueous drops. The thickness of the oil film between drops decreases and ruptures. As a consequence, the drops coalesce together. However, if the electric field is too high, the resultant drop deforms and disintegrates, reducing the efficiency of the electrocoalescence process. In this work, a multiple-physic simulation is performed to determine the critical electric field that causes the disintegration of an aqueous drop. The configuration is an aqueous drop, located on a grounded electrode in an insulating oil under an electric field. We numerically analyze the deformation and disintegration of a drop under the applied electric field using the finite element method. We use the level set method to track the interface between the aqueous drop and the insulating oil. The results show that the aqueous drop elongates due to the electric force and starts to deform under 0.36 kV/mm electric field. The elongation and the deformation of drop increase with the electric field. Eventually, the aqueous drop completely loses its stability at the critical field value, which is equal to 0.9 kV/mm. An experiment is conducted to observe the disintegration of drop. The experimental results demonstrate that the drop disintegration occurs at the average electric field of 1.02 kV/mm, slightly higher than the critical field obtained from the simulation. [ABSTRACT FROM AUTHOR]

This study proposes an electrode shape optimization strategy with a space-charge distribution. Previous studies on electrode shape optimization did not consider field distortion through a space charge. Here, a non-dimensional charge transport equation and a Poisson equation are numerically solved in a fully coupled system using a standard finite element method (FEM) for transient analysis. The proposed method can be used to generate or smoothen the target field of a particular part in a strongly inhomogeneous field, such as dry etching reactor. The deformations of the design parameters are determined based on the velocity field calculated through a sensitivity analysis and are expressed using the level set method. To verify its feasibility, the proposed method was tested on a planer diode model. The results show that the optimized shape is well-matched with the analytical solution. Application models, such as a sharp pin-plane model, were also tested to demonstrate the utility of the proposed method. [ABSTRACT FROM AUTHOR]

Spring is a widely used mechanical equipment. The segmentation of spring images is seriously affected by intensity inhomogeneity. To solve this problem, a two-stage high-precision spring image segmentation algorithm is proposed. First, a spring measurement platform is constructed to obtain images of springs. Then a two-stage spring image contour extraction algorithm is proposed. The first stage combines the global information of the image to construct a histogram fuzzy clustering model to obtain the initial contour of the spring, then, the local information of the spring image is combined to construct a level set model to accurately extract the spring contour in the second stage. Through comparison with existing algorithms and accuracy analysis experiments, the effectiveness of the method is verified. Experimental results show that the algorithm has high accuracy, strong adaptive ability, high running speed, and can effectively suppress the problem of intensity inhomogeneity. [ABSTRACT FROM AUTHOR]

Segmentation of brain tumors is important for medical diagnosis, treatment planning, and disease development. However, the presence of image artifacts such as noise, intensity inhomogeneity, and partial volume effect in real-world images can aggressively affect the segmentation task. The manual segmentation of the tumor is highly error-prone and is a time-consuming task. Hence, in this work, we propose a methodology that can identify and segment the brain tumor slices in magnetic resonance (MRI) images. The work is composed of three main stages namely pre-processing, segmentation, and post-processing. In the pre-processing stage, N4ITK is applied to correct the intensity inhomogeneity and an Anisotropic diffusion filter is used to remove the noise present in MR images. In the segmentation stage, an active contour model that combines the region scalable fitting energy (RSF) and optimized laplacian of gaussian energy (OLoG) is proposed to segment the tumor region from MRI. The proposed segmentation method first presents an LoG energy term optimized by an energy functional that can smooth the homogeneous regions and enhance edge information simultaneously. Next, the optimized LoG energy term is combined with the RSF energy term which utilizes the local region information to drive the curve towards the boundaries. With the addition of the LoG term, the proposed model is insensitive to the positions of the initial contour and produces an accurate segmentation result. Finally, morphological operations and thresholding are used to extract the tumor region from the segmented image which is computed previously. The performance of the proposed segmentation method is evaluated using the BRATS, and J.Cheng dataset. The obtained experimental results demonstrate that our proposed method significantly outperforms the manual process and state of the art methods in brain tumor segmentation. [ABSTRACT FROM AUTHOR]

In clinical applications of cardiac left ventricle (LV) segmentation, the segmented LV is desired to include the cavity, trabeculae, and papillary muscles, which form a convex shape. However, the intensities of trabeculae and papillary muscles are similar to myocardium. Consequently, segmentation algorithms may easily misclassify trabeculae and papillary muscles as myocardium. In this paper, we propose a level set method with a convexity preserving mechanism to ensure the convexity of the segmented LV. In the proposed level set method, the curvature of the level set contours is used to control their convexity, such that the level set contour is finally deformed as a convex shape. The experimental results and the comparison with other level set methods show the advantage of our method in terms of segmentation accuracy. Compared with the state-of-the-art methods using deep-learning, our method is able to achieve comparable segmentation accuracy without the need for training, while the deep-learning based method requires a large set of training data and high-quality manual segmentation. Therefore, our method can be conveniently used in situation where training data and their manual segmentation are not available. [ABSTRACT FROM AUTHOR]

Multi-scale spatial representation has been widely used in geographic information and online mapping systems. Terrain contour, which provides a reference for understanding and monitoring the Earth's surface, is an important data category. For the multi-scale representation of contour lines, simplification is a fundamental step in providing different levels of detail for linear features. However, achieving a global continuous multi-scale simplification of contours remains a challenge. Therefore, based on the concept of level set, a novel contour simplification method labeled the continuous changing surface model (CCSM) was proposed in this paper. The CCSM was built by using a non-uniform rational B-spline constrained with characteristics and was then intersected with a set of horizontal planes with progressive height values. The generated intersection lines are considered continuous multi-scale simplified contours. Experiments were conducted on a 1:50,000 real contour dataset to verify the effectiveness of CCSM. Results showed that the changes in the shape of the simplified contours generated by CCSM are more natural and progressive than those generated by two other significant simplification methods. CCSM can also effectively balance local and global structures and has potential applications in obtaining a continuous multi-scale representation of terrain contours. [ABSTRACT FROM AUTHOR]

This paper presents an approach for pulse rate extraction from videos. The core of the presented approach is a novel method to segment and track a suitable region of interest (ROI). The proposed method combines level sets with subject-individual Gaussian Mixture Models to yield a time varying ROI. The ROI builds up from multiple homogeneous skin areas under constraints regarding the area and contour length of the ROI. Together with state of the art signal processing methods our approach yields an Mean Average Error (MAE) of 2.3 bpm, 1.4 bpm and 2.7 bpm on own data, the PURE database and the UBFC-rPPG database, respectively. Therewith, our method performs equal or better compared to widely used approaches (e.g. the KLT tracker instead of the proposed image processing yields an MAE of 2.6 bpm, 2.6 bpm and 4.4 bpm). Such results and the 2nd place with a MAE of 7.92 bpm in the 1st Challenge on Remote Physiological Signal Sensing prove the applicability of the proposed method. The taken approach, however, bears further potential for optimization in the context of photoplethysmography imaging and should be transferable to other segmentation tasks as well. [ABSTRACT FROM AUTHOR]

Accurate lymphoma segmentation on Positron Emission Tomography (PET) images is of great importance for medical diagnoses, such as for distinguishing benign and malignant. To this end, this paper proposes an adaptive weighting and scalable distance regularized level set evolution (AW-SDRLSE) method for delineating lymphoma boundaries on 2D PET slices. There are three important characteristics with respect to AW-SDRLSE: 1) A scalable distance regularization term is proposed and a parameter q can control the contour's convergence rate and precision in theory. 2) A novel dynamic annular mask is proposed to calculate mean intensities of local interior and exterior regions and further define the region energy term. 3) As the level set method is sensitive to parameters, we thus propose an adaptive weighting strategy for the length and area energy terms using local region intensity and boundary direction information. AW-SDRLSE is evaluated on 90 cases of real PET data with a mean Dice coefficient of 0.8796. Comparative results demonstrate the accuracy and robustness of AW-SDRLSE as well as its performance advantages as compared with related level set methods. In addition, experimental results indicate that AW-SDRLSE can be a fine segmentation method for improving the lymphoma segmentation results obtained by deep learning (DL) methods significantly. [ABSTRACT FROM AUTHOR]

Under complicated forest stands like natural forest, mixed forest, and multi-storied forest, the visible light forest images are greatly influenced by the canopy density, canopy structure, and photographing season, etc. During the crown extraction, the existing methods fail to guarantee the precision and lack effective manual intervention mechanism. A universal, flexible, and practical crown segmentation method, which could realize the automatic segmentation under low canopy density and appropriately implement the manual intervention under high canopy density, was explored in this study. The unmanned aerial vehicle (UAV) visible light forest images were firstly processed into DSM, DEM, and DOM, the CHM was obtained by deducting DEM from DSM, the plane position of tree top was extracted from CHM via the local maximum method to generate a Thiessen polygon, a rectangle of crown range was generated based on its bounding rectangle, the image of crown range of an individual standing tree was traversed and segmented, and after the anisotropic diffusion filtering, the boundary curve of crown was evolved out through the level set method. A level set CV model-based crown segmentation plug-in that could operate with embedded ArcMap was implemented on ArcGIS Engine via C# language, this plug-in was used to do the crown extraction test of DOM images in nine 50 m×50 mstandard mixed forest sample plots with different canopy densities and different species compositions Naimuhe forest farm of Inner Mongolia Great Khingan Dayangshu Forestry Bureau, and meanwhile, this method was compared with the manual extraction method and SVM image segmentation method. The results showed that the extraction rate of the proposed method was averagely elevated by 45.97% in comparison with that of the manual extraction method, and the extraction accuracy was averagely improved by 15.29 percentage pionts compared with that of the SVM image segmentation method. Directing at the problems existing in the crown extraction from UAV forest visible light images of natural mixed forests, namely, the extraction difficulty was great and the existing methods were of low precision, a method integrating level set and selective manual intervention was proposed in this study, thus effectively avoiding the large workload in the full manual segmentation, and solving the problem that the precision could not be guaranteed by the machine learning method, which was inconvenient for the manual intervention. The level set method, which was not influenced by the initial value, was used, so it was unnecessary to seek for the initial value by training a large quantity of samples, and the crown segmentation efficiency was improved. The crown segmentation result is the vector line factor of the crown boundary of an individual standing tree, which can flexibly edit individual crowns. This method highlights the efficiency under low canopy density and large crown breadth, and guarantees the accuracy under high canopy density and small crown breadth, so it is of strong flexibility and universality. [ABSTRACT FROM AUTHOR]

Image segmentation is an important analysis tool in the field of computer vision. In this paper, on the basis of the traditional level set method, a novel segmentation model using generalized divergences is proposed. The main advantage of generalized divergences is their smooth connection performance among various kinds of well-known and frequently used fundamental divergences with one formula. Therefore, the discrepancy between two probability distributions of segmented image parts can be measured by generalized divergences. We also found a solution to determine the optimal divergence automatically for different images. Experimental results on a variety of synthetic and natural images are presented, which demonstrate the potential of the proposed method. Compared with the previous active contour models formulated to solve the same nonparametric statistical segmentation problem, our method performs better both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

This article presents a B-spline level set (BLS)-based reconstruction scheme for multiphase conductivity imaging with electrical impedance tomography (EIT). The EIT inverse problem is recast as a shape reconstruction problem where both the inclusion interface and conductivity values are the unknown. In using this scheme, the unknown conductivity to be reconstructed is assumed to be piecewise constant while the inclusion interface is represented by the zero level set of a BLS surface, which, in turn, is represented as a linear combination of B-spline basis functions. Through such a low-order representation technique, the number of unknowns in the reconstruction problem is significantly reduced compared with the traditional pixel-/voxel-based reconstruction methods. The performance of the BLS scheme is tested with heart-and-lungs phantom studies and the cross section of the industrial pipeline phantom studies. The results demonstrate that the BLS scheme is capable of preserving sharp features of inclusions and providing estimates to both the interface locations and unknown conductivity values with reliable accuracy. In addition, we also found that the BLS scheme is quite robust to the selection of the regularization weighting parameter in the least-square-based optimization regime and the additional parameter in the Heaviside function used for modeling the conductivity profile. [ABSTRACT FROM AUTHOR]

Featured Application: Functionally graded materials composed of cellular structures have the characteristics of high specific stiffness/strength, energy absorption, etc. On the occasion of their structural lightweight and energy absorption, functionally graded materials are often used in engineering. In recent years, the functionally graded materials (FGM) with cellular structure have become a hot spot in the field of materials research. For the continuously varying cellular structure in the layer-wise FGM, the connection of gradient cellular structures has become the main problem. Unfortunately, the effect of gradient connection method on the overall structural performance lacks attention, and the boundary mismatch has enormous implications. Using the homogenization theory and the level set method, this article presents an efficient topology optimization method to solve the connection issue. Firstly, a simple but efficient hybrid level set scheme is developed to generate a new level set surface that has the partial features of two candidate level sets. Then, when the new level set surface is formed by considering the level set functions of two gradient base cells, a special transitional cell can be constructed by finding the zero level set of this generated level set surface. Since the transitional cell has the geometric features of two gradient base cells, the shape of the transitional cell fits perfectly with its connected gradient cells on both sides. Thus, the design of FGM can have a smooth connectivity with C1 continuity without any complex numerical treatments during the optimization. A number of examples on both 2D and 3D are provided to demonstrate the characteristics of the proposed method. Finite element simulation has also been employed to calculate the mechanical properties of the designs. The simulation results show that the FGM devised by the proposed method exhibits better mechanical performances than conventional FGM with only C0 continuity. [ABSTRACT FROM AUTHOR]

Quantitative assessment of retinal layer thickness in spectral domain-optical coherence tomography (SD-OCT) images is vital for clinicians to determine the degree of ophthalmic lesions. However, due to the complex retinal tissues, high-level speckle noises and low intensity constraint, how to accurately recognize the retinal layer structure still remains a challenge. To overcome this problem, this paper proposes an adaptive-guided-coupling-probability level set method for retinal layer segmentation in SD-OCT images. Specifically, based on Bayes's theorem, each voxel probability representation is composed of two probability terms in our method. The first term is constructed as neighborhood Gaussian fitting distribution to characterize intensity information for each intra-retinal layer. The second one is boundary probability map generated by combining anatomical priors and adaptive thickness information to ensure surfaces evolve within a proper range. Then, the voxel probability representation is introduced into the proposed segmentation framework based on coupling probability level set to detect layer boundaries. A total of 1792 retinal B-scan images from 4 SD-OCT cubes in healthy eyes, 5 cubes in abnormal eyes with central serous chorioretinaopathy and 5 SD-OCT cubes in abnormal eyes with age-related macular disease are used to evaluate the proposed method. The experiment demonstrates that the segmentation results obtained by the proposed method have a good consistency with ground truth, and the proposed method outperforms six methods in the layer segmentation of uneven retinal SD-OCT images. [ABSTRACT FROM AUTHOR]

Summary: In this article, we proposed an improved region‐based active contour model based on curve evolution theory and variational level set method. Our method can be used to segment image with intensity inhomogeneity, such as medical computed tomography images. Our model contains a local intensity fitting term that makes the evolution curve stop at boundaries of the object and a global expansion term that makes the evolution curve have the chance to get to every location in the image. Therefore, our model has a good performance to solve the problem of flexible initialization, which exposed in region‐scalable fitting energy model. For the curvature term that occurs during the calculation, we calculated it with a more efficiency and accuracy method. Compared with other models, our model shows good segmentation result and less computation expense. Finally, we will present some experimental results, especially the result of contrast experiment. [ABSTRACT FROM AUTHOR]

Summary: We present a robust numerical method for solving incompressible, immiscible two‐phase flows. The method extends both a monolithic phase conservative level set method with embedded redistancing and a semi‐implicit high‐order projection scheme for variable‐density flows. The level set method can be initialized conveniently via a simple phase indicator field instead of a signed distance function (SDF). To process the indicator field into a SDF, we propose a new partial differential equation‐based redistancing method. We also improve the monolithic level set scheme to provide more accuracy and robustness in full two‐phase flow simulations. Specifically, we perform an extra step to ensure convergence to the signed distance level set function and simplify other aspects of the original scheme. Lastly, we introduce consistent artificial viscosity to stabilize the momentum equations in the context of the projection scheme. This stabilization is algebraic, has no tunable parameters and is suitable for unstructured meshes and arbitrary refinement levels. The overall methodology includes few numerical tuning parameters; however, for the wide range of problems that we solve, we identify only one parameter that strongly affects performance of the computational model and provide a value that provides accurate results across all the benchmarks presented. This methodology results in a robust, accurate, and efficient two‐phase flow model, which is mass‐ and volume‐conserving on unstructured meshes and has low user input requirements, making it attractive for real‐world applications. [ABSTRACT FROM AUTHOR]

Purpose: Automated segmentation could improve the efficiency of modeling‐based pelvic organ prolapse (POP) evaluations. However, segmentation performance is limited by the blurry soft tissue boundaries. In this study, we aimed to present a hybrid solution for uterus, rectum, bladder, and levator ani muscle segmentation by combining a convolutional neural network (CNN) and a level set method. Methods: We used 24 sagittal pelvic floor magnetic resonance (MR) series from six anterior vaginal prolapse and six posterior vaginal prolapse subjects (a total 528 MR images). The stress MR images were performed both at rest and at maximal Valsalva. We assigned 264 images for training, 132 images for validation, and 132 images for testing. A CNN was designed by introducing a multi‐resolution features pyramid module (MRFP) into an encoder‐decoder model. Depth separable convolution and pretraining were used to improve model convergence. Multiclass cross entropy loss and multiclass Dice loss were used for model training. The dice similarity coefficient (DSC) and average surface distance (ASD) were used for evaluating the segmentation results. To prove the effectiveness of our model, we compared it with advanced segmentation methods including Deeplabv3+, U‐Net, and FCN‐8s. The ablation study was designed to quantify the contributions of MRFP, the encoder network, and pretraining. Besides, we investigated the working mechanism of MRFP in the segmentation network by comparing our model with three of its variants. Finally, the level set method was used to improve the CNN model further. Results: Dice loss showed better segmentation performance than multiclass cross entropy loss. MRFP was efficacious for different encoder networks. With MRFP, U‐Net and U‐Net‐X (X represents Xception encoder network) have improved the DSC, on average by 6.8 and 5.3 points. Compared with different CNN models, our model achieved the highest average DSC of 65.6 points and the lowest average ASD of 2.9 mm. With the level set method, the DSC of our model improved to 69.4 points. Conclusions: MRFP proved to be effective in addressing the blurry soft tissue boundary problem on pelvic floor MR images. A hybrid solution based on CNN and level set method was presented for pelvic organ segmentation both at rest and at maximal Valsalva; with this method, we achieved state‐of‐the‐art results. [ABSTRACT FROM AUTHOR]