Finite element simulation of skeletal muscular structures obtained from images of histological serial sections

Weichert, Frank; Schröder, Jörg; Landes, Constantin A.; Walczak, Lars; Müller, Heinrich; Wagner, Mathias

doi:10.1016/j.jbiomech.2010.01.046

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…Segmentation of oval or round structures, e.g. cross‐sections of nuclei and/or nucleoli (de Solorzano et al ., ), in images of biologically relevant scenarios is one of the most essential and challenging tasks in the previously described concept of morphology‐based systems biological mathematical modelling (Wagner et al ., ; Weichert et al ., , , b). Methods using morphological segmentation, especially the watershed algorithm, to detect nuclei have appeared in various publications, e.g.…”

Section: Introductionmentioning

confidence: 99%

Bayesian model for detection and classification of meningioma nuclei in microscopic images

Wirjadi

Kim

Stech

et al. 2016

Journal of Microscopy

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Image segmentation aims to determine structures of interest inside a digital picture in biomedical sciences. State-of-the art automatic methods however still fail to provide the segmentation quality achievable by humans who employ expert knowledge and use software to mark target structures on an image. Manual segmentation is time-consuming, tedious and suffers from interoperator variability, thus not serving the requirements of daily use well. Therefore, the approach presented here abandons the goal of full-fledged segmentation and settles for the localization of circular objects in photographs (10 training images and 20 testing images with several hundreds of nuclei each). A fully trainable softcore interaction point process model was hence fit to the most likely locations of nuclei of meningioma cells. The Broad Bioimage Benchmark Collection/SIMCEP data set of virtual cells served as controls. A 'colour deconvolution' algorithm was integrated to determine (based on anti-Ki67 immunohistochemistry) which real cells might have the potential to proliferate. In addition, a density parameter of the underlying Bayesian model was estimated. Immunohistochemistry results were 'simulated'for the virtual cells. The system yielded true positive (TP) rates in the detection and classification of real nuclei and their virtual counterparts. These hits outnumbered those obtained from the public domain image processing software ImageJ by 10%. The method introduced here can be trained to function not only in medicine and morphology-based systems biology but in other application domains as well. The algorithm lends itself to an automated approach that constitutes a valuable tool which is easy to use and generates acceptable results quickly.

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Section: Introductionmentioning

confidence: 99%

Bayesian model for detection and classification of meningioma nuclei in microscopic images

Wirjadi

Kim

Stech

et al. 2016

Journal of Microscopy

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“…The physical behavior resulting from the presence of the fibers, which is a kind of anisotropy, can be taken into account considering the preferential orientation of the fibers through a vector oriented in the direction of the fibers [7]. In this context, the simplest type of anisotropy, transverse isotropy, is present in many biological tissues [8] and can be considered simply by introducing directly into the deformation energy equation a portion of additional deformation energy that depends on the preferential direction of the material in the reference configuration, as implemented by some authors [1,9,10].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Composites Structural Analysis With Viscoelastic Fibers

Sinaceur¹,

Sahli²,

Sahli³

2018

Adv. Sci. Technol. Res. J.

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This paper presents a formulation for material and geometrical nonlinear analysis of composite materials by immersion of truss finite elements into triangular 2D solid ones using a novel formulation of the finite element method based on positions. This positional formulation uses the shape functions to approximate some quantities defined in the Nonlinear Theory of Elasticity and proposes to describe the specific strain energy and the potential of the external loads as function of nodal positions which are set from a deformation function. Because the nodal positions have current values in each node, this method naturally considers the geometric nonlinearities while the nonlinear relationships between stress and strain may be considered by a pure nonlinear elastic theory called hyperelasticity which allows to obtain linearised constitutive laws in its variational form. This formulation should be able to include both viscoelastic and active behavior, as well as to allow the consideration of nonlinear relations between stresses and deformations. It is common to adopt hyperelastic constitutive laws. Few are the works that use the strategy of approaching the problem such as fibers immersed in a matrix. The immersion of fibers in the matrix makes it possible to include both a viscoelastic behavior in a simple and direct way. The examples are simple cases, some of them even with analytical solutions, mainly for validation purposes of the presented formulations. By modeling a structure, the examples show the potentialities of the concepts and proposed formulations.

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“…Visando a estabilidade do método dos elementos finitos, Weichert et al (2010) propôs um método de reconstrução de malhas 3D a partir de cortes seriados em tecidos biológicos. A lei constitutiva hiperelástica de Mooney-Rivlin com energia de deformação decomposta em uma parcela isovolumétrica e outra volumétrica foi adotada na validação do método de geração de malha proposto.…”

Section: Revisão Bibliográficaunclassified

“…Essas teorias viscoelásticas são desenvolvidas em duas linhas: modelos diferenciais (Maxwell e Voigt) ou integrais (Boltzman) (HUMPHREY, 2003). Os modelos hiperelásticos descrevem razoavelmente o comportamento muscular em nível macroscópio e podem, inclusive, ser combinados com modelos viscoelásticos a fim de representar melhor o comportamento em função de taxas (WEICHERT et al, 2010).…”

Section: Leis Constitutivas Para Tecidos Muscularesunclassified

“…O comportamento físico resultante da presença das fibras, que é de certa forma um tipo de anisotropia, pode ser levado em conta considerando a orientação preferencial das fibras através de um vetor orientado na direção das mesmas (WEICHERT et al, 2010). Nesse contexto, o tipo mais simples de anisotropia, a isotropia transversal, está presente em muitos tecidos biológicos (HUMPHREY, 2003) e pode ser considerada simplesmente introduzindo-se diretamente na equação da energia de deformação uma parcela de energia de deformação adicional que depende da direção preferencial do material na configuração de referência, conforme feito por alguns autores (WEISS;MAKER;GOVINDJEE, 1996;MARTINS et al, 1998;TANG;ZHANG;TSUI, 2009;CALVO et al, 2010;WEICHERT et al, 2010). A definição de novos invariantes dados em função das direções nas quais as fibras imersas estão orientadas permite definir parcelas adicionais de energia de deformação de uma forma compacta, admitindo inclusive considerar materiais com fibras orientadas em diversas direções (CHAGNON; REBOUAH; FAVIER, 2015).…”

Section: Capítulo 1 Introduçãounclassified

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Análise de estruturas planas reforçadas com fibras ativas viscoelásticas e matriz com modelo constitutivo hiperelástico: aplicações gerais em engenharia e biomecânica

Friedel¹

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Finite element simulation of skeletal muscular structures obtained from images of histological serial sections

Cited by 5 publications

References 26 publications

Bayesian model for detection and classification of meningioma nuclei in microscopic images

Bayesian model for detection and classification of meningioma nuclei in microscopic images

Nonlinear Composites Structural Analysis With Viscoelastic Fibers

Análise de estruturas planas reforçadas com fibras ativas viscoelásticas e matriz com modelo constitutivo hiperelástico: aplicações gerais em engenharia e biomecânica

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